Imaging device and method for generating images of specimens

Abstract

An imaging device for generating images of a specimen is disclosed. Layers of images can be captured by an optical system and then compiled to create an integrated image. Each layer may contain a different focus. A combined image can then be created by combining one or more integrated images.

Description

Technical Field

[0001] The present invention generally relates to the field of real-time image generation. In particular, the present invention is directed to an imaging device and method for generating an image of a specimen having variable thickness.

Background Art

[0002] Extended depth of focus techniques for digital acquisition of pathological slides are useful. However, extending the depth of focus is difficult and cumbersome when generating an image of a specimen having variable thickness.

Summary of the Invention

[0003] In one aspect, an imaging device for generating an image of a specimen is described. The imaging device receives a first parameter set associated with a first location of the specimen, receives a second parameter set associated with at least one second location of the specimen, and according to the first parameter set, captures a first plurality of images of a first target area at the first location of the specimen, and according to the second parameter set, captures a second plurality of images of a second target area at the second location of the specimen, compiles the first plurality of images into a first integrated image and the second plurality of images into a second integrated image, combines the first integrated image and the second integrated image into a combined image, and includes a circuit configuration configured to display the combined image.

[0004] In another embodiment, a method for generating an image of a specimen is described. The method includes: receiving a first parameter set related to a first location of a specimen by an imaging device; receiving a second parameter set related to a second location of a specimen by an imaging device; capturing a first set of images of a first target area at the first location of the specimen according to the first parameter set, and capturing a second set of images of a second target area at the second location of the specimen according to the second parameter set; compiling the first set of images into a first integrated image and the second set of images into a second integrated image by a processor; combining the first integrated image and the second integrated image into a combined image by a processor; and displaying the combined image by a processor.

[0005] In another embodiment, another imaging device for image generation is described. The imaging device includes an optical system; a slide, comprising a slide port configured to hold a slide on which a specimen is placed; an actuator mechanism mechanically connected to a moving element; a processing module; at least one processor; and a memory communicably connected to the at least one processor, which includes instructions for receiving a first parameter set relating to a first location of a specimen, receiving a second parameter set relating to a second location of a specimen, using the optical system to capture a first plurality of images of a first target area at the first location of the specimen according to the first parameter set, using at least one processor to compile the first plurality of layers into a first integrated image, using the optical system to capture a second plurality of images of a second target area at a second location of the specimen according to a second parameter set, using at least one processor to compile the second plurality of layers into a second integrated image, combining the first integrated image and the second integrated image into a combined image, and displaying the combined image to the user by an output interface.

[0006] In another embodiment, another imaging device for generating images of a sample is described. The imaging device receives a first parameter set related to a first location of the sample, receives a second parameter set related to at least one second location of the sample, captures a first plurality of images of a first target area at the first location of the sample according to the first parameter set, and a second plurality of images of a second target area at the second location of the sample according to the second parameter set, compiles the first plurality of images into a first combined image and the second plurality of images into a second combined image, compares the first combined image with a goodness-of-fit scale, and determines if the first combined image is outside a predetermined threshold of the goodness-of-fit scale. The present invention includes a circuit configuration configured to flag a first integrated image, combine the first integrated image and a second integrated image into a combined image, and display the combined image, wherein comparing the first integrated image involves using a goodness-of-fit machine learning model, using a goodness-of-fit machine learning model involves training a goodness-of-fit machine learning model using goodness-of-fit scale training data which includes multiple data entries, each containing multiple integrated image inputs correlated to a goodness-of-fit scale output, and using the trained goodness-of-fit machine learning model to flag the first integrated image in accordance with a comparison between the first integrated image and the goodness-of-fit scale.

[0007] In another aspect, another method for generating images of a sample is described. The method includes: receiving a first parameter set related to a first location of the sample by an imaging device; receiving a second parameter set related to a second location of the sample by an imaging device; capturing a first set of images of a first target area at the first location of the sample according to the first parameter set, and a second set of images of a second target area at the second location of the sample according to the second parameter set; compiling the first set of images into a first combined image and the second set of images into a second combined image by a processor; comparing the first combined image with a goodness-of-fit measure by a processor; and The process includes flagging the first combined image by a losser if the first combined image is outside a predetermined threshold of the goodness-of-fit scale, combining the first combined image and the second combined image into a merged image by a processor, and displaying the merged image by a processor, comparing the first combined image includes using a goodness-of-fit machine learning model, using a goodness-of-fit machine learning model includes training a goodness-of-fit machine learning model using goodness-of-fit scale training data which includes multiple data entries, each containing multiple combined image inputs correlated to a goodness-of-fit scale output, and using the trained goodness-of-fit machine learning model to flag the first combined image in accordance with a comparison between the first combined image and the goodness-of-fit scale.

[0008] In another embodiment, another imaging device for image generation is described. The imaging device includes an optical system, a slide port configured to hold a slide on which a specimen is placed, at least one processor, and a memory communicably connected to the at least one processor, which receives a first parameter set relating to a first location of a specimen, receives a second parameter set relating to a second location of a specimen, uses the optical system to capture a first set of images of a first target area at the first location of the specimen according to the first parameter set, compiles the first set of images into a first combined image, compares the first combined image to a goodness-of-fit scale, flags the first combined image if it falls outside a predetermined threshold of the goodness-of-fit scale, and uses the optical system to process images according to the second parameter set. The system includes memory and memory, which includes instructions to configure at least one processor to capture a second set of images of a second target area at a second location of a sample, compile the second set of layers into a second combined image, combine the first combined image and the second combined image into a merged image, and display the merged image to the user via an output interface; comparing the first combined image includes using a goodness-of-fit machine learning model, using a goodness-of-fit machine learning model includes training a goodness-of-fit machine learning model using goodness-of-fit scale training data which includes a plurality of data entries including a plurality of combined image inputs correlated to a goodness-of-fit scale output, and using the trained goodness-of-fit machine learning model to flag the first combined image in accordance with a comparison between the first combined image and the goodness-of-fit scale.

[0009] In another embodiment, another imaging device for image generation is described. The imaging device includes a circuit configuration configured to receive a first parameter set relating to a first target area of ​​a specimen, the first parameter set including a first depth of field. The imaging device includes a circuit configuration configured to receive a second parameter set relating to a first target area of ​​a specimen, the second parameter set including a second depth of field. The imaging device includes a circuit configuration configured to capture a first plurality of images of the first target area of ​​a specimen according to the first parameter set. The imaging device includes a circuit configuration configured to capture a second plurality of images of the first target area of ​​a specimen according to the second parameter set. The imaging device includes a circuit configuration configured to remove a first artifact in the first plurality of images using an artifact machine learning model, the artifact machine learning model including a generative machine learning model. The imaging device includes a circuit configuration configured to remove a second artifact in a second plurality of images using an artifact machine learning model. The imaging device includes a circuit configuration configured to compile the first plurality of images into a first unified image. The imaging device includes a circuit configuration configured to compile a second set of images into a second unified image. The imaging device includes a circuit configuration configured to combine the first unified image and the second unified image into a combined image. The imaging device includes a circuit configuration configured to display the combined image.

[0010] In another embodiment, another method of image generation is described. The method includes receiving a first parameter set, which includes a first depth of field, related to a first target area of ​​a sample, by an imaging device. The method includes receiving a second parameter set, which includes a second depth of field, related to a first target area of ​​a sample, by an imaging device. The method includes capturing a first plurality of images of the first target area of ​​a sample according to the first parameter set, by an imaging device. The method includes capturing a second plurality of images of the first target area of ​​a sample according to the second parameter set, by an imaging device. The method includes removing a first artifact in the first plurality of images by an imaging device using an artifact machine learning model, which includes a generative machine learning model. The method includes removing a second artifact in a second plurality of images by an imaging device using an artifact machine learning model. The method includes removing the first plurality of images into a first combined image by an imaging device. The method includes compiling a second set of images into a second unified image using an imaging device. The method also includes combining the first unified image and the second unified image into a combined image using an imaging device. Finally, the method includes displaying the combined image.

[0011] In one or more embodiments, the imaging device may include a circuit configuration configured to receive a first set of parameters associated with a first location of a specimen. In one or more embodiments, the imaging device may include a circuit configuration configured to receive a second set of parameters associated with at least one second location of a specimen. In one or more embodiments, the imaging device may include a circuit configuration configured to capture a first set of images of a first area of ​​interest at a first location of a specimen according to the first set of parameters. In one or more embodiments, the imaging device may include a circuit configuration configured to capture a second set of images of a second area of ​​interest at a second location of a specimen according to a second set of parameters. The first set of parameters may include one or more focal lengths associated with each image of the first set of images. The first set of parameters may include parameters associated with some of the images in the first set of images. The first set of parameters may include nominal focal lengths associated with some of the images in the first set of images. In one or more embodiments, the imaging device may include a circuit configuration configured to process the first set of images by determining the degree of quality of depiction of the area of ​​interest in one or more of the images in the first set of images. In one or more embodiments, the imaging device may include a circuit configuration configured to compile a first set of images into a first unified image. In one or more embodiments, the imaging device may include a circuit configuration configured to compile a second set of images into a second unified image. In one or more embodiments, the imaging device may include a circuit configuration configured to compare the first unified image to a fitness scale and flag the first unified image if it falls outside a predetermined threshold of the fitness scale.In one or more embodiments, the imaging device may include a circuit configuration configured to update the first multiple images if the first combined image is flagged, compile the updated first multiple images to create an updated combined image, compare the updated combined image to a fitness scale, and identify an optimized combined image if the updated combined image falls within a predetermined threshold of the fitness scale. In one or more embodiments, the imaging device may include a circuit configuration configured to combine the first combined image and the second combined image into a merged image. In one or more embodiments, the imaging device may include a circuit configuration configured to display the merged image. In one or more embodiments, the imaging device may include a circuit configuration configured to receive a third set of parameters related to a second area of ​​interest of a specimen, capture a third set of images of the second area of ​​interest of the specimen according to the third set of parameters, and compile the third set of images into a third combined image. The third set of parameters may include a third focal length / depth. In one or more embodiments, the imaging device may include a circuit configuration configured to move the optical system from a first location to a second location according to a second set of parameters. In one or more embodiments, the imaging device may include a circuit configuration configured to move the optical system from a first target area to a second target area according to a third set of parameters. In one or more embodiments, the imaging device may include a circuit configuration configured to move the optical system from a first location to a second location. In one or more embodiments, the imaging device may include an optical system, a slide port configured to hold a slide on which a specimen is placed, at least one processor, and a memory communicably connected to the processor, which includes instructions that configure the processor to perform one or more functions of the imaging device.In one or more embodiments, the imaging device may include an actuator mechanism, and capturing a first plurality of images includes using the actuator mechanism to position a slide relative to an optical system such that a first target area defined by a first set of parameters lies within the line of sight or line of sight of the optical sensor of the optical system, and setting the zoom of the optical system based on the zoom level specified by the first set of parameters. In one or more embodiments, the imaging device may include a circuit configuration configured to remove a first artifact from a first plurality of images by inputting the first plurality of images into an artifact machine learning model and removing the first artifact in the first plurality of images using an artifact removal machine learning model. In one or more embodiments, the imaging device is configured to combine a first integrated image, a second integrated image, and a third integrated image into a merged image. In one or more embodiments, the imaging device may include a circuit configuration configured to update the first multiple images if the first integrated image is flagged, compile the updated first multiple images to create an updated integrated image, compare the updated integrated image to a fitness scale, and identify an optimized integrated image if the updated integrated image falls within a predetermined threshold of the fitness scale.

[0012] Details of one or more variations of the subject matter described herein are shown in the accompanying drawings and the following description. Other features and advantages of the subject matter described herein will become apparent from the description and drawings, as well as the claims.

[0013] For the purpose of illustrating the present invention, the drawings illustrate aspects of one or more embodiments of the present invention. However, it should be understood that the present invention is not limited to the exact arrangement and means shown in the drawings. [Brief explanation of the drawing]

[0014] [Figure 1A]These are various schematic diagrams of exemplary embodiments of imaging devices for generating images of specimens according to one or more embodiments of the present disclosure. [Figure 1B] These are various schematic diagrams of exemplary embodiments of imaging devices for generating images of specimens according to one or more embodiments of the present disclosure. [Figure 1C] These are various schematic diagrams of exemplary embodiments of imaging devices for generating images of specimens according to one or more embodiments of the present disclosure. [Figure 2] This is a block diagram of an exemplary embodiment of a machine learning process according to one or more embodiments of the present disclosure. [Figure 3] This is a block diagram of an exemplary embodiment of a score database according to one or more embodiments of the present disclosure. [Figure 4] This is a diagram illustrating an exemplary embodiment of a neural network according to one or more embodiments of the present disclosure. [Figure 5] This is a diagram illustrating an exemplary embodiment of a node in a neural network according to one or more embodiments of the present disclosure. [Figure 6] This figure illustrates an exemplary embodiment of fuzzy set comparison according to one or more embodiments of the present disclosure. [Figure 7] This is a flowchart illustrating an exemplary method for generating images of a specimen according to one or more embodiments of the present disclosure. [Figure 8] This is a block diagram of a computer system that may be used to implement any one or more of the methodologies disclosed herein and any one or more of them. [Modes for carrying out the invention]

[0015] Drawings are not necessarily to scale and may be represented by imaginary lines, schematics, and partial drawings. In some cases, details not necessary for understanding the embodiment, or details that would make it difficult to perceive other details, may be omitted. Similar reference numerals in different drawings indicate similar elements.

[0016] Broadly speaking, embodiments of this disclosure relate to imaging devices and methods for generating images of specimens. More specifically, embodiments of this disclosure relate to imaging devices and methods for enhancing extended depth of focus (EDOF) techniques for digital imaging of pathological glass slides. In one or more embodiments, the imaging device enables the acquisition of specimens with variable thickness and can leverage focus information from acquired locations to dynamically configure the size of the stacked images collected for use in acquiring subsequent locations. Exemplary embodiments illustrating embodiments of this disclosure are described below in the context of several specific examples.

[0017] Referring here to Figure 1A, an exemplary embodiment of an imaging device 100 for generating an image of a specimen having variable thickness is shown. In one or more embodiments, the imaging device 100 may include optical instruments. For example, the imaging device 100 may include a microscope, for example. In one or more embodiments, the imaging device 100 may include an application-specific integrated circuit (ASIC). The ASIC may be communicatively connected to a memory such as memory 108. The memory may include read-only memory (ROM) and / or rewritable ROM, FPGA, or other combinations and / or sequentially synchronous or asynchronous digital circuit configurations for storing parameters further described in this disclosure. In one or more embodiments, the memory may include one or more memory devices for storing data and information such as parameters or metrics. One or more memory devices may include, but are not limited to, various types of memory, including volatile and non-volatile memory devices such as ROM (Read-Only Memory), EEPROM (Electrically-Erasable Read-Only Memory), RAM (Random Access Memory), and flash memory. In one or more embodiments, the processor is adapted to execute software stored in memory to perform various methods, processes, and modes of operation in the method described herein. In other embodiments, the imaging device 100 may include a circuit configuration. For example, but are not limited, the imaging device 100 may include programming in the software and / or hardware circuit design. In one or more embodiments, the imaging device 100 may include a processor 104. The processor 104 may include any processor 104 described herein, but is not limited to.Processor 104 includes, or may include, any computing device described in this disclosure, including, but not limited to, the microcontrollers, microprocessors, digital signal processors (DSPs), and / or system-on-a-chip (SoCs) described herein. The computing device includes, or is included in, a mobile device such as a mobile phone or smartphone, and / or can communicate with a mobile device. Processor 104 may include a single computing device operating independently, or it may include two or more computing devices operating in cooperation, in parallel, sequentially, etc. Two or more computing devices may be included together in a single computing device or two or more computing devices. Processor 104 may interface with or communicate with one or more additional devices via a network interface device, as described in more detail below. The network interface device may be used to connect Processor 104 to one or more of various networks and one or more devices. Examples of network interface devices include, but are not limited to, network interface cards (e.g., mobile network interface cards, LAN cards), modems, and any combination thereof. Examples of networks include, but are not limited to, wide area networks (e.g., the Internet, corporate networks), local area networks (e.g., networks associated with offices, buildings, campuses, or other relatively small geographical spaces), telephone networks, data networks associated with telephone / voice operators (e.g., mobile carrier data and / or voice networks), direct connections between two computing devices, and any combination thereof. Networks can use wired and / or wireless communication modes. In general, any network topology can be used.Information (e.g., data, software, etc.) can communicate between a computer and / or a computing device. Processor 104 can include, but is not limited to, for example, a computing device or a cluster of computing devices at a first location, and a second computing device or a cluster of computing devices at a second location. Processor 104 can include one or more computing devices dedicated for data storage, security, distribution of traffic for load balancing, etc. Processor 104 can distribute one or more of the computing tasks described below across multiple computing devices that can operate in parallel, in series, redundantly, or in any other manner used for task or memory distribution between computing devices. Processor 104 may be implemented using a "nothing shared" architecture where data is cached at the worker, which in one embodiment may enable scalability of the imaging device 100 and / or the computing device.

[0018] Continuing with reference to Figure 1A, the processor 104 may be designed and / or configured to perform any method, process, or series of process steps in any embodiment of the disclosure in any order and to any degree of repetition. For example, the processor 104 may be configured to repeatedly perform a single process or series of processes until a desired or instructed result is achieved. The repetition of a process or series of processes is performed by repeatedly and / or recursively using the output of the previous repetition as input to the subsequent repetition, aggregating the inputs and / or outputs of the repetitions to produce an aggregated result, reducing or decreasing one or more variables such as global variables, and / or dividing a larger processing task into a set of smaller, repeatedly addressed processing tasks. The processor 104 may execute any process or series of processes in parallel, such as by using two or more parallel threads, processor cores, etc., to execute the process simultaneously and / or substantially simultaneously multiple times. Task division between parallel threads and / or processes may be performed according to any protocol suitable for task division between iterations. Those skilled in the art, upon reviewing the entirety of this disclosure, will recognize a variety of ways in which processes, sets of processes, processing tasks, and / or data can be subdivided, shared, or otherwise processed using iterative, recursive, and / or parallel processing.

[0019] Continuing to refer to Figure 1A, the imaging device 100 includes a memory 108. The memory 108 is communicatively connected to a processor 104. The memory may include instructions that configure the processor 104 to perform tasks disclosed in this disclosure. Where used in this disclosure, “communicatively connected” means connected by a connection, attachment, or link between two or more related things that enable the reception and / or transmission of information between them. For example, but not limited to, this connection may be a wired or wireless, direct or indirect connection that enables the reception and / or transmission of (one or more) data and / or signals between two or more components, circuits, devices, systems, imaging devices, etc. The data and / or signals between them may include, but not limited to, electrical, electromagnetic, magnetic, video, audio, radio and microwave data and / or signals, or combinations thereof. The communication connection may be achieved, for example, directly or through one or more intervening devices or components via wired or wireless electronic, digital or analog communication, but not limited to. Furthermore, a communication connection may include electrically coupling or connecting at least one output of one device, component, or circuit to at least one input of another device, component, or circuit. For example, this may be done via a bus or other equipment for communication between elements of a computing device, for example, but not limited to. A communication connection may also include indirect connections via, for example, wireless connections, radio communications, low-power wide-area networks, optical communications, magnetic coupling, capacitive coupling, or optical coupling, for example, but not limited to. In some cases, the term “communicatively coupled” may be used in place of “communicatively connected” in this disclosure.

[0020] Referring further to FIG. 1A, the imaging device 100 can include one or more sensors for capturing an image signal representative of a scene (e.g., a scene including the specimen 112). For example, without limitation, the sensors can include optical sensors, image sensors (further described below), focal plane arrays, and the like. In various embodiments, the sensors can be provided to represent and / or convert the captured image signal of the scene into digital data. For example, without limitation, the sensors can include analog-to-digital converters. In one or more embodiments, the processor 104 is adapted to receive an image signal from the imaging device 100 (e.g., an image sensor), process the image signal to provide processed image data, store the image signal and / or the image data in the memory 108, and / or retrieve the stored image signal and / or image data from the memory 108 (for compilation or combination, as further discussed in the present disclosure). In one or more embodiments, the processor 104 can be configured to process the image signal stored in the memory 108 to provide image data for display for viewing by a user and / or an operator.

[0021] Referring further to FIG. 1A, in one or more embodiments, the imaging device can include a display further described in FIG. 8 and / or can be communicatively connected to a display. In one or more embodiments, the display can be configured to display image data and any other information described in the present disclosure, such as annotations or text. In one or more embodiments, the processor 104 can be configured to retrieve image data and information from the memory 108 and display such image data and information on the display. In other embodiments, the display can receive image data directly from an optical system, such as the optical system 120 (e.g., an optical sensor).

[0022] Referring further to Figure 1A, the imaging device 100 may include user input and / or a user interface. For example, but not limited to, the user interface may include one or more user-operated components such as one or more push buttons, joysticks, sliders, rotary knobs, mice, keyboards, touchscreens, etc., which may be configured to generate one or more input control signals, and the input control signals may include signals for capturing images from a scene, combining images and / or image data, compiling images and / or image data, changing the operating mode of the imaging device, zooming and / or changing the zoom level, changing the focus, etc. User input signals may be generated using the user interface and transmitted to the processor 104, memory 108, display, optical system 120, and / or any other components of the imaging device 100 and / or any other components that are communicably connected to the imaging device 100. In one or more embodiments, the processor may be configured to change or set operating modes of the imaging device, such as autofocus, contrast, gain (e.g., variable gain), field of view (FOV), brightness, offset, menu activation and selection, spatial settings, and temporal settings, but is not limited to these.

[0023] Continuing to refer to Figure 1A, in some embodiments, an imaging device 100 can be used to generate an image of the specimen 112. For the purposes of this disclosure, “specimen” is a sample of organic material used for testing or observation purposes. In one or more embodiments, the specimen may include a pathological sample. For example, but not limited to, the specimen may include a sample of interest containing tissue, plasma, or bodily fluids from an individual. For example, but not limited to, the specimen 112 may include tissue from an organ such as the kidney of an individual (e.g., a patient). In some embodiments, the specimen 112 may include a tissue sample. In some embodiments, the specimen 112 may be frozen. In some embodiments, the specimen 112 may be fresh or recently acquired. In one or more embodiments, the specimen 112 may include variable thickness. For example, but not limited to, the specimen 112 may have different thicknesses or depths at various locations along the specimen 112. For example, but not limited to, specimen 112 may have a first thickness t at a first location x, a second thickness t' at a second location x', and a third thickness t' at a third location x''.

[0024] Continuing with reference to Figure 1A, in one or more embodiments, the specimen 112 may be placed on a slide 116. As used in this disclosure, “slide” is a container or surface for holding a specimen. In some embodiments, the slide 116 may include a formalin-fixed paraffin-embedded slide. In some embodiments, the specimen 112 on the slide 116 may be stained. In some embodiments, the slide 116 may be substantially transparent. In some embodiments, the slide 116 may include a glass slide. In some embodiments, the slide 116 may include a thin, flat, substantially transparent glass slide. In some embodiments, a cover, such as a transparent cover, may be added to the slide 116 so that the specimen 112 is placed between the slide 116 and the cover. For example, but not limited to, the specimen 112 may be compressed between the slide 116 and the corresponding cover.

[0025] Referring further to Figure 1A, in some embodiments, slide 116 and / or the sample on slide 116 may be illuminated. In some embodiments, the imaging device 100 may include a light source. As used in this disclosure, “light source” is any device configured to emit electromagnetic radiation. In some embodiments, the light source may emit light having substantially one wavelength. In some embodiments, the light source may emit light having a wavelength range. The light source may emit ultraviolet light, visible light, and / or infrared light, but is not limited. In non-limiting examples, the light source may include a light-emitting diode (LED), an organic LED (OLED), and / or any other light emitter. Such a light source may be configured to illuminate slide 116 and / or the sample 112 on slide 116. In non-limiting examples, the light source may illuminate slide 116 and / or the sample 112 on slide 116 from below. In a non-limiting example, the light source can illuminate slide 116 and / or specimen 112 on slide 116 from above.

[0026] Referring further to Figure 1A, in some embodiments, the imaging device 100 may include at least one optical system 120. As used in this disclosure, “optical system” is an arrangement of one or more components that act together with or use electromagnetic radiation, such as light (e.g., visible light, infrared light, UV light, etc.). The optical system 120 may include, but is not limited to, one or more optical elements, such as lenses, mirrors, windows, filters, etc. The optical system 120 may form an optical image corresponding to an optical object. For example, but is not limited to, the optical system 120 may form an optical image in or on an optical sensor that can capture an optical image, for example, digitize it. In some cases, the optical system 120 may have at least one magnification. For example, but is not limited to, the optical system 120 may include an objective lens (e.g., a microscope objective lens) and one or more re-imaging optical elements that work together to produce optical magnification. In some cases, the degree of optical magnification may be referred to herein as zoom. As used herein, “optical sensor” is a device that measures light and converts the measured light into one or more signals. The one or more signals may include, but are not limited to, one or more electrical signals. In some embodiments, the optical sensor may include at least one photodetector. As used herein, “photodetector” is a device that is sensitive to light and thereby capable of detecting light. In some embodiments, the photodetector may include a photodiode, photoresistor, light sensor, photovoltaic chip, etc. In some embodiments, the optical sensor may include multiple photodetectors. The optical sensor may include, but is not limited to, a camera. The optical sensor may communicate electronically with at least one processor 104 of the imaging device 100. As used herein, as used in this disclosure, “electronic communication” is a shared data connection between two or more devices. In some embodiments, the imaging device 100 may include two or more optical sensors.

[0027] Referring further to Figure 1A, in some embodiments the optical system 120 may include a camera. In some cases, the camera may include one or more optical systems. Exemplary, non-limiting optical systems include spherical lenses, aspherical lenses, reflectors, polarizers, filters, windows, aperture diaphragms, etc. In some embodiments, in non-limiting examples, one or more optical systems associated with the camera may be adjusted to change the camera's zoom, depth of field, and / or focal length. In some embodiments, one or more of such settings may be configured to detect features of a sample on slide 116. In some embodiments, one or more of such settings may be configured based on a set of parameters, as described below. In some embodiments, the camera may be able to capture an image at a low depth of field. In non-limiting examples, the camera may be able to capture an image so that it is in focus at a first sample depth and out of focus at a second sample depth. In some embodiments, an autofocus mechanism may be used to determine the focal length. In some embodiments, the focal length may be set by a set of parameters. In some embodiments, the camera may be configured to capture multiple images at different focal lengths. In non-limiting examples, a camera may capture multiple images at different focal lengths such that at least one image captures an image in focus at each sample depth of focus of the sample. In some embodiments, a camera may include at least one image sensor. Exemplary non-limiting image sensors include, but are not limited to, digital image sensors such as charge-coupled device (CCD) sensors and complementary metal-oxide-semiconductor (CMOS) sensors. In some embodiments, but are not limited to, a camera may be sensitive to electromagnetic radiation in the invisible range, such as infrared radiation.

[0028] Referring further to Figure 1A, in some embodiments, the imaging device 100 may include a machine vision system. The machine vision system may include an optical system 120, or may be communicably connected to the optical system 120, a processor 104, a memory 108, etc. In some embodiments, the machine vision system may include at least one camera. The machine vision system may use images, such as images from at least one camera, to make decisions about a scene, space, and / or objects. For example, the machine vision system may be used for world modeling or alignment of objects in space. In some cases, alignment may include, but are not limited to, image processing such as object recognition, feature detection, and edge / corner detection. Non-exclusive examples of feature detection include scale-invariant feature transform (SIFT), Canny edge detection, and C-Tomasi corner detection. In some cases, alignment may include one or more transformations for oriented the camera frame (or image or video stream) to a three-dimensional coordinate system. Exemplary transformations, but are not limited to, include homography and affine transformations. In one embodiment, the alignment of the first frame to a coordinate system can be verified and / or corrected using object recognition and / or computer vision, as described above. For example, an initial alignment to two dimensions, expressed as, for example, alignment to x and y coordinates, may be performed using a two-dimensional projection of a three-dimensional point onto the first frame. A third dimension of alignment, representing depth and / or the z axis, can be detected by comparing two frames. For example, if the first frame includes a pair of frames captured using a pair of cameras (e.g., stereoscopic cameras, also referred to in this disclosure as stereo cameras), a pair of stereoscopic views of the image of the object can be detected using image recognition and / or edge detection software. By comparing the two stereoscopic views, z-axis values ​​of points on the object can be derived, and further z-axis points inside and / or around the object can be derived using interpolation, for example.This can be repeated with multiple objects in the field of view, including, but not limited to, environmental features of interest identified by an object classifier and / or indicated by an operator. In one embodiment, the x and y axes can be selected to span a common plane and / or the xy-plane of the first frame for two cameras used for stereoscopic image capture. As a result, in the case of an affine transformation of the object's coordinates, as also described above, the x and y translation components and φ can be pre-inputted into the translation matrix and rotation matrix. The initial x and y coordinates and / or estimations in the transformation matrix may, alternatively or additionally, be performed between the first and second frames, as described above. As described above, for each of multiple points on the object and / or one or more edges of the object, the x and y coordinates of the first stereoscopic frame are set, and an initial estimate of the z coordinate is given based on assumptions about the object, such as the assumption that the ground is substantially parallel to the xy-plane, as selected above. Next, the z-coordinate and / or x, y, and z-coordinates registered using the image capture and / or object recognition process described above can be compared with the predicted coordinates using the initial inference in the transformation matrix. An error function can be calculated by comparing two sets of points, and new x, y, and / or z-coordinates can be iteratively estimated and compared until the error function falls below a threshold level. In some cases, the machine vision system may use a classifier, such as any classifier described throughout this disclosure. As used in this disclosure, the z-axis is the axis perpendicular to the xy-plane, and therefore to the top surface of slide 116.

[0029] Continuing to refer to Figure 1A, the optical system 120 may be configured to capture images of the area of ​​interest 128, such as images 136a-e in Figure 1B. For example, but not limited to, the camera of the optical system 120 may be configured to capture images of the area of ​​interest. For the purposes of this disclosure, “area of ​​interest” is an area of ​​scene or environment that is selected or desired to be located within the line of sight, and therefore within the FOV 156 of the optical components of the optical system. For the purposes of this disclosure, “line of sight” is a line along which an observer or lens has a field of view that is not obstructed. “Field of view” is, for the purposes of this disclosure, the angle and / or area in which the optical components detect electromagnetic radiation. For example, but not limited to, the FOV can indicate an area of ​​scene that can be captured by the optical components within a defined boundary of an image (e.g., a frame). For example, but not limited to, the area of ​​interest 128 within the FOV 156 of the optical system 120 may include a scene that is desired to be captured in the image by being located within the line of sight of the optimal lens of the system 120 so that the image can be captured. The FOV 156 may include vertical and horizontal angles projected onto the surface of the lens of the optical component. In one or more embodiments, the line of sight may include the optical access of the FOV. In various embodiments, the area 128 to be targeted may include at least a portion of the specimen 112. In some embodiments, the area 128 to be targeted may include a portion of the specimen 112 and a portion of the slide 116.

[0030] Referring further to Figure 1A, in one or more embodiments, an image may include image data. As used in this disclosure, “image data” is information representing at least one physical scene, space, and / or object. The image data may include, for example, information representing a sample, slide 116, or an area of ​​the sample or slide. In some cases, the image data may be generated by a camera. “Image data” may be used interchangeably with “image” throughout this disclosure where image is used as a noun. An image may be an optical image, for example, when at least one optical element is used to generate an image of an object. An image may be a digital image, for example, when it is represented as a bitmap. Alternatively, an image may consist of any medium that can represent a physical scene, space, and / or object. Alternatively, when “image” is used as a verb in this disclosure, it refers to the generation and / or formation of an image.

[0031] Referring further to Figure 1A, in some embodiments, the imaging device 100 may include a slide port 144. In some embodiments, the slide port 144 may be configured to hold a slide 116. In some embodiments, the slide port 144 may include one or more alignment features. As used herein, “alignment features” are physical features that help to fix a slide in place and / or align the slide with another component of the imaging device. In some embodiments, the alignment features may include components that keep the slide 116 fixed, such as a clamp, latch, clip, recessed area, or another fastener. In some embodiments, the slide port 144 may allow for easy removal or insertion of the slide 116. In some embodiments, the slide port 144 may include a transparent surface through which light can pass. In some embodiments, the slide 116 may be placed on such a transparent surface and / or illuminated by light passing through it. In some embodiments, the slide port 144 may be mechanically connected to an actuator mechanism 124 as described below.

[0032] Referring further to Figure 1A, in some embodiments, the imaging device 100 may include an actuator mechanism 124. As used herein, “actuator mechanism” is a mechanical component configured to change the position of a slide relative to an optical system. In one or more embodiments, the actuator mechanism can be used to change the line of sight so that an image of a new area of ​​interest (e.g., a second area of ​​interest) can be captured, as further described herein. In some embodiments, the actuator mechanism 124 may be mechanically connected to a slide 116, such as a slide 116 in a slide port 144. In some embodiments, the actuator mechanism 124 may be mechanically connected to a slide port 144. For example, the actuator mechanism 124 may move the slide port 144 in order to move the slide 116. For example, but not limited to, the actuator mechanism 124 may move the slide port 144 so that the distance D between the top surface of the slide 116 and the lens of the optical system 120 changes. In other embodiments, the actuator mechanism 124 can also change the angle between the top surface (e.g., a surface directed towards or facing the optical system, which is the surface in contact with the specimen) and the lens. In some embodiments, the actuator mechanism 124 may be mechanically connected to at least one optical system 120. In some embodiments, the actuator mechanism 124 may be mechanically connected to a movable element. When used in this disclosure, the movable element may include, but is not limited to, a movable or portable object, component, and / or device within the imaging device 100, such as a slide, slide port, or optical system. In some embodiments, the movable element can be moved so that the optical system 120 is properly positioned relative to the slide 116 so that the optical system 120 can capture an image of the slide 116 according to a set of parameters.In some embodiments, the actuator mechanism 124 may be mechanically connected to an item selected from a list consisting of a slide port 144, a slide 116, and at least one optical system 120. In some embodiments, the actuator mechanism 124 may be configured to change the relative position between the slide 116 and the optical system 120 by moving the slide port 144, the slide 116, and / or the optical system 120.

[0033] Referring further to Figure 1A, the actuator mechanism 124 may include mechanical components responsible for the movement and / or control of the mechanism or system. In some embodiments, the actuator mechanism 124 may require a control signal and / or an energy or power source. In some cases, the control signal may be of relatively low energy. Exemplary forms of control signals include electric potential or current, pneumatic pressure or flow rate, hydraulic fluid pressure or flow rate, mechanical force / torque or speed, or even human power. In some cases, the actuator may have an energy or power source other than the control signal. This may include a primary energy source that can include, for example, electric power, hydraulic power, pneumatic power, or mechanical power. In some embodiments, upon receiving a control signal, the actuator mechanism 124 responds by converting power into mechanical motion. In some cases, the actuator mechanism 124 may be understood as a form of automation or automatic control.

[0034] Referring further to Figure 1A, in some embodiments, the actuator mechanism 124 may include a hydraulic actuator. The hydraulic actuator may consist of a cylinder or fluid motor that uses hydraulic power to facilitate mechanical movement. The output of the hydraulic actuator mechanism 124 may include, but is not limited to, mechanical motion such as linear motion, rotational motion, or oscillating motion. In some embodiments, the hydraulic actuator may use a liquid hydraulic fluid. Since the fluid is incompressible in some cases, the hydraulic actuator may exert a large force. Furthermore, since force is equal to pressure multiplied by area, the hydraulic actuator can act as a force transducer with a change in area (e.g., the cross-sectional area of ​​the cylinder and / or piston). An exemplary hydraulic cylinder may consist of a hollow cylindrical tube on which a piston can slide. In some cases, the hydraulic cylinder may be considered single-acting. "Single-acting" can be used when the fluid pressure is applied to substantially only one side of the piston. Thus, a single-acting piston can move in only one direction. In some cases, a spring may be used to give the single-acting piston a return stroke. In some cases, the hydraulic cylinder may be considered double-acting. "Double-acting" can be used when pressure is applied to substantially both sides of the piston. The piston is moved by the difference in the resultant force between the two sides of the piston.

[0035] Referring further to Figure 1A, in some embodiments, the actuator mechanism 124 may include a pneumatic actuator mechanism. In some cases, a pneumatic actuator may be able to generate considerable force from relatively small changes in gas pressure. In some cases, a pneumatic actuator may be able to respond more quickly than other types of actuators, such as hydraulic actuators. A pneumatic actuator may be able to use a compressible fluid (e.g., air). In some cases, a pneumatic actuator may be able to operate with compressed air. The operation of a hydraulic and / or pneumatic actuator may include the control of one or more valves, circuits, fluid pumps, and / or fluid manifolds.

[0036] Referring further to Figure 1A, in some embodiments, the actuator mechanism 124 may include an electromechanical actuator. The electromechanical actuator mechanism 124 may include either an electromechanical actuator or a linear motor. In some cases, the actuator mechanism 124 may include an electromechanical actuator. An electromechanical actuator can convert the rotational force of an electric rotary motor into linear motion to generate linear motion through the mechanism. Exemplary mechanisms, but not limited to, include rotation-to-translational motion converters such as belts, screws, cranks, cams, linkage mechanisms, and Scotch yokes. In some cases, control of the electromechanical actuator may include control of an electric motor, for example, a control signal may control one or more electric motor parameters to control the electromechanical actuator. Exemplary, non-limiting electric motor parameters include rotational position, input torque, speed, current, and potential. The electromechanical actuator mechanism 124 may also include a linear motor. A linear motor may differ from an electromechanical actuator because the power from a linear motor is output directly as translational motion, rather than being output as rotational motion and converted into translational motion. In some cases, a linear motor may result in lower friction losses than other devices. Linear motors can be further divided into at least three distinct categories, including flat linear motors, U-channel linear motors, and tubular linear motors. Linear motors can be directly controlled by control signals for controlling one or more linear motor parameters. Exemplary linear motor parameters include, but are not limited to, position, force, velocity, potential, and current.

[0037] Referring further to Figure 1A, in some embodiments, the actuator mechanism 124 may include a mechanical actuator mechanism 124. In some cases, the mechanical actuator mechanism 124 may function to perform motion by converting one type of motion, such as rotational motion, into another type of motion, such as linear motion. An exemplary mechanical actuator includes a rack and pinion. In some cases, a mechanical power source, such as a power take-off device, may function as a power source for the mechanical actuator. The mechanical actuator may use any number of mechanisms, including, but not limited to, gears, rails, pulleys, cables, linkage mechanisms, etc.

[0038] Referring further to Figure 1A, in some embodiments, the actuator mechanism 124 can electronically communicate with an actuator control. As used herein, “actuator control” is a system configured to operate the actuator mechanism so that the slide and the optical system reach a desired relative position. In some embodiments, the actuator control can operate the actuator mechanism 124 based on inputs received from a user interface. In some embodiments, the actuator control may be configured to operate the actuator mechanism 124 so that the optical system 120 is in a position to capture an image of the entire sample. In some embodiments, the actuator control may be configured to operate the actuator mechanism 124 so that the optical system 120 is in a position to capture an image of the area of ​​interest. As used herein, “area of ​​interest” (also referred to herein as “area of ​​interest”) is a specific area in a digital image or a specific area in a slide. In some embodiments, the area of ​​interest may include an area selected or navigated by the user. Electronic communication between the actuator mechanism 124 and the actuator control may include the transmission of signals. For example, the actuator control may generate physical movement of the actuator mechanism in response to an input signal. In some embodiments, the input signal may be received by the actuator control from the processor 104 or the input interface.

[0039] Referring further to Figure 1A, where used in this disclosure, “signal” is any understandable representation of data, for example, from one device to another. Signals can include optical signals, hydraulic signals, pneumatic signals, mechanical signals, electrical signals, digital signals, analog signals, and the like. In one or more embodiments, image data may be transmitted via one or more signals. In other embodiments, commands from an operator of imaging device 100 can be transmitted via one or more signals to components of the imaging device, such as an optical system. In some cases, signals can be used to communicate with a computing device, for example, via one or more ports. In some cases, signals may be transmitted and / or received by a computing device, for example, via input / output ports. Analog signals can be digitized, for example, by an analog-to-digital converter. In some cases, analog signals may be processed before digitization by any analog signal processing steps described in this disclosure, for example. In some cases, digital signals can be used to communicate between two or more devices, including, but not limited to, a computing device. In some cases, digital signals may be communicated by one or more communication protocols, including but not limited to the Internet Protocol (IP), Controller Area Network (CAN) protocol, serial communication protocols (e.g., universal asynchronous receiver-transmitter: [UART]), and parallel communication protocols (e.g., IEEE [PrinterPort]).

[0040] Referring further to Figure 1A, in some embodiments, the imaging device 100 can perform one or more signal processing steps on a signal. For example, the imaging device 100 can analyze, modify, and / or synthesize a signal representing data to improve the signal, for example, by improving transmission, storage efficiency, or signal-to-noise ratio. Exemplary methods of signal processing can include analog, continuous-time, discrete, digital, nonlinear, and statistical. Analog signal processing may be performed on non-digitized or analog signals. Exemplary analog processes can include passive filters, active filters, adders, mixers, integrators, delay lines, companders, multipliers, voltage-controlled filters, voltage-controlled oscillators, and phase-locked loops. Continuous-time signal processing may, in some cases, be used to process signals that change continuously within a domain, such as time. Exemplary, non-restrictive continuous-time processes can include time-domain processing, frequency-domain processing (Fourier transform), and complex frequency-domain processing. Discrete-time signal processing may be used when a signal is sampled discontinuously or at discrete time intervals (i.e., temporally quantized). Analog discrete-time signal processing can process signals using sample-and-hold circuits, analog time-division multiplexers, analog delay lines, and analog feedback shift registers, as illustrated in the following example circuits. Digital signal processing can be used to process digitized discrete-time sampled signals. Generally, digital signal processing may be performed by computing devices such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or dedicated digital signal processors (DSPs), or other dedicated digital circuits, but is not limited to these. Digital signal processing can be used to perform any combination of typical arithmetic operations, including fixed-point and floating-point, real and complex numbers, multiplication, and addition. Digital signal processing can further operate circular buffers and lookup tables.Further non-restrictive examples of algorithms that can be performed according to digital signal processing techniques include the fast Fourier transform (FFT), finite impulse response (FIR) filters, infinite impulse response (IIR) filters, and adaptive filters such as Wiener filters and Kalman filters. Statistical signal processing can be used to process signals as random functions (i.e., stochastic processes) by leveraging statistical properties. For example, in some embodiments, the signal may be modeled with a probability distribution exhibiting noise, which may then be used to reduce the noise in the processed signal.

[0041] Referring further to Figure 1A, in some embodiments, the imaging device 100 may include a user interface, as previously described in this disclosure. The user interface may include an output interface and an input interface. In some embodiments, the output interface may include one or more elements on which the imaging device 100 can communicate information to the user. In an indefinite example, the output interface may include a display. The display may include a high-resolution display. The display may output images, videos, etc., to the user. In another indefinite example, the output interface may include a speaker. The speaker may output sound to the user. In yet another indefinite example, the output interface may include a haptic device. The speaker may output haptic feedback to the user.

[0042] Referring further to Figure 1A, in some embodiments, the input interface may include controls for operating the imaging device 100. Such controls may be operated by the user. In non-limiting examples, the input interface may include a camera, microphone, keyboard, touchscreen, mouse, joystick, foot pedal, buttons, dial, etc. In non-limiting examples, the input interface may accept machine input, voice input, visual input, text input, etc. In some embodiments, voice input to the input interface may be interpreted using an automatic voice recognition function, enabling the user to control the imaging device 100 via voice. In some embodiments, the input interface may be similar to the controls of a microscope.

[0043] Referring further to Figure 1A, in one or more embodiments, the imaging device 100 may be configured to create a multilayer scan, which includes multiple, for example, a series of images combined into a single image. The multilayer scan may include an integrated image. For example, but not limited to, a multilayer scan includes a compilation of sequential images imaged at different levels along the z-axis or depth axis at a specific location (x, y) of a specimen. For example, but not limited to, as further described below, a multilayer scan may include multiple images, such as an image imaged at depth A, an image imaged at depth B, and an image imaged at depth C, and so on.

[0044] Referring here to Figure 1A, in various embodiments, the imaging device 100 can receive one or more parameters from the operator of the imaging device 100. One or more parameters may include multiple parameters (also referred to herein as a “parameter set”). As used in this disclosure, a “parameter set” is, but is not limited to, a set of values ​​such as quantitative and / or numerical values ​​that identify how an image is captured. A parameter set may be implemented as a data structure, as described later. In some embodiments, the imaging device 100 can receive a parameter set from the operator using an input interface. A parameter set may include x and y coordinates indicating a location on the specimen that the operator intends to view (e.g., a target area 128). For example, but is not limited to, the imaging device 100 can receive parameters such as a first location x related to a first target area 128a and a second location x' related to a second target area 128b (shown in Figure 1B) from user input, a database, or other means described herein. For example, but not limited to, a user interface can be used to select a first location x and a second location x' as parameters based on the field of view (e.g., field of view) of the optical components. In other embodiments, the first location x and the second location x' can be determined using a trained machine learning model, such as the machine learning model described in Figure 2. For example, but not limited to, the first location x and the second location x' can be determined by determining the field of view required to cover a sample or the items to be captured in the sample (e.g., specimen 112). The parameter set may include desired depth of field and zoom levels. For the purposes of this disclosure, “depth of field” (also referred to herein as depth of focus) is the distance between a lens, a set of lenses, a focusing mirror, and / or a set of focusing mirrors and an image sensor of the optical system to achieve a particular focus.As used in this disclosure, “zoom level” is data relating to the magnification of an area within the line of sight of the optical system. For example, though not limited to, the zoom level may include the magnification of the area of ​​interest within the line of sight. The zoom level may include optical zoom and / or digital zoom. In an unspecified example, the zoom level may be “8x” zoom. The parameter set may include a desired depth of field, as described in this disclosure. In an unspecified example, an operator may operate the input interface and / or imaging device 100 so that the parameter set includes x and y coordinates and a zoom level corresponding to a more zoomed-in view of a particular area of ​​the sample. In some embodiments, the parameter set corresponds to a more zoomed-in view of a particular area of ​​the sample contained in the first image. This can be done, for example, to obtain a more detailed figure of at least a portion of the specimen 112. As used in this disclosure, unless otherwise indicated, “x coordinate” and “y coordinate” refer to coordinates along axes perpendicular to each other, and the plane defined by these axes is parallel to the plane of the surface of the slide 116. In some cases, setting the zoom level may include changing one or more optical elements of the optical system 120. For example, setting the zoom level may include, but is not limited to, replacing a first objective lens with a second objective lens having a different magnification. Additionally or alternatively, the entire magnification of the optical system can be changed by "downbeam" replacement of one or more optical components from the objective lenses, thereby setting the zoom level. In some cases, setting the zoom level may include changing the digital magnification. The digital magnification may include outputting the image at a different resolution, i.e., after rescaling the image, using the output interface.

[0045] Referring further to Figure 1B, in some embodiments, the imaging device 100 may be configured to determine one or more parameters of a first multilayer scan 140a (also referred to herein as the “integrated image”) at a first location x of the specimen 112. The first multilayer scan 140a may include a plurality of first images 136a-e related to a first target area 128a of the specimen 112 at the first location x. For example, but not limited to, the first multilayer scan 140a may include the first target area 128a among a plurality of target areas 128a-c. In one or more embodiments, the processor 104 of the imaging device 100 may be configured to determine one or more parameters of the first multilayer scan 140a at the first location x of the specimen 112. In one or more embodiments, the multilayer scan may include a plurality of images, as shown in Figure 1B. In one or more embodiments, the parameters may include measurable factors and / or conditions for imaging an image of a desired location of the specimen. The parameters for a multilayer scan can include a number of layers (e.g., one or more layers) each at a different focal plane and / or focal point of a focal plane. The number of layers can include multiple layers and / or images. In a non-limiting exemplary embodiment, the parameters for a multilayer scan 140a could include a first image 136a at focal point A imaged at a first location x as a first layer, a second image 136b at focal point B as a second layer, a third image 136c at focal point C as a third layer, a fourth image 136d at focal point D as a fourth layer, a fifth image 136e at focal point E as a fifth layer, and so on. In some embodiments, the foci are equally spaced. In other embodiments, the foci may be at various distances from each other. The parameters for a multilayer scan can also include the size of each layer and / or image. For example, but not limited to, the size of each layer and / or image may include the dimensions (in units) of the area of ​​interest of the specimen 112. The parameters for a multilayer scan can also include a number of layers, as described above. In one or more embodiments, each image can be captured dynamically, and the images may include video over a certain period of time (e.g., a temporal parameter).

[0046] Referring further to Figure 1B, in some embodiments, determining one or more parameters of the first multilayer scan 140a at a first location x of the specimen 112 may include determining a counting metric of the first multilayer image scan 140a. For the purposes of this disclosure, “counting metric” is a parameter relating to quantity. The counting metric may include a number of layers to be included in the multilayer scan, such as multiple images, or images of varying depths. In some embodiments, the processor 104 may receive one or more counting metrics from the operator. In other embodiments, the processor 104 may determine the counting metric. In one or more embodiments, the quantity of layers can be determined by estimating the depth (e.g., thickness) of the specimen 112. In one or more non-limiting embodiments, the depth of the specimen 112 can be determined by subtracting the thickness Ts of the slide 116 on which the specimen is placed and the thickness Tc of the coverslip from the combined thickness TT of the slide and coverslip, i.e., Ds = TT - (Ts + Tc). Next, the depth Ds of the specimen can be divided into specific depths of field and intervals (i.e., step-by-step) in the objective lens. In some embodiments, each image of the multiple images may have a different focal point. The focal distance between each image can be controlled using the imaging device, for example, by using a motor to move a slide horizontally and / or vertically relative to the lens of the imaging device, such as an actuator mechanism 124. In other embodiments, detecting one or more parameters may include detecting the thickness t of the specimen in the area of ​​interest. For example, but not limited to, a light sensor, pressure sensor, infrared sensor, etc., can be used to determine the height or thickness of the specimen 112 at any location along the specimen 112.

[0047] Referring further to Figure 1B, in some embodiments, determining one or more parameters of the first multilayer scan 140a at a first location x of the specimen 112 may include determining the focus metric for each layer of the first multilayer image scan. Focuses such as A, B, C, D, and E in Figure 1B may be determined according to the focus metric. The focus can be adjusted by moving the specimen 112 along the z-axis relative to the lens of the imaging device, for example, using an actuator mechanism 124. The focus metric for each layer may be determined based on creating a focal plane in which at least a portion of the specimen 112 is in focus on the selected focal plane.

[0048] Referring further to Figure 1B, in one or more embodiments, the imaging device 100 may be configured to capture a first plurality of images 136 of the specimen at a first location x. In various embodiments, the optical system 120 may capture at least one first image 136a of the specimen 112 at a first location x of the specimen 112 in a first target area 128a. For example, but not limited to, the optical system 120 may capture a first plurality of images 136 of the first target area 128a while the first target area 128a is positioned within the line of sight 132 of the optical system 120. In some embodiments, capturing a first plurality of images 136a of the first target area 128a while the slide 116 is in a first position may include using an actuator mechanism 124 and / or actuator control to move the optical system 120 and / or the slide 116 to a desired position. The desired position may include, for example, but is not limited to, the location and / or orientation of slide 116 and / or specimen 112 in space defined by a Cartesian coordinate system (x, y, z), a polar coordinate system (r, θ), a cylindrical coordinate system (ρ, φ, z), or a spherical coordinate system (r, θ, φ). In some embodiments, each image 136a-e of the first plurality of images may include an image of the entire sample and / or slide 116. In other embodiments, each image 136a-e of the first plurality of images may include an image of a region of the sample. In some embodiments, one image of the first plurality of images may include an image at a wider angle than another image of the first plurality of images. In some embodiments, one image of the first plurality of images may include an image with a lower resolution than another image. In some embodiments, a machine vision system and / or optical character recognition system may be used to determine one or more features of the sample and / or slide 116. Such feature determination may be used, for example, to remove artifacts from the image and / or to annotate the image, as described below.In a non-limiting example, an optical character recognition system could be used to identify writing on slide 116, which could then be used to annotate the image on slide 116. In another non-limiting example, a machine vision system could be used to detect dust particles within slide 116, and an artifact removal machine learning model (described later) could then remove the dust particles from the image.

[0049] Referring further to Figure 1B, the first plurality of images 136 may include one or more images 136a-e with the same x and y coordinates at different depths of focus along the z axis. As used in this disclosure, “depth of focus” is the depth at which the optical system is in focus on at least a portion of the specimen. The depth of focus may include the depth at which a particular focus is achieved. As used in this disclosure, “focal length” is the distance f from the optical system at which the optical sensor is in focus. For example, but not limited to, the first image 136a and the second image 136b of the first plurality of images 136 may have different focal lengths and / or depths of focus. In some embodiments, such images may be stored as multiple layers of a multilayer scan. For example, but not limited to, each image may include a layer of the first plurality of images corresponding to a particular depth of focus. In some embodiments, the operator may navigate between layers using a user interface, such as a user interface of a remote device communicably connected to a computing system or processor 104 and memory 108. In an indefinite example, this may be done by displaying a depth of focus selection menu to the user. In this example, the user can select a first depth of focus and view an image digitized at that first depth of focus. In this example, the operator can select a second depth of focus and view an image digitized at that second depth of focus. In some embodiments, the imaging device 100 can capture an image at a specific depth of focus selected by the user.

[0050] Referring further to Figure 1B, in one or more embodiments, the imaging device 100 may be configured to compile a first plurality of images 136 into a first combined image 140a. For the purposes of this disclosure, “combined image” is a processed multilayer scan consisting of a plurality of images imaged at various depths of focus. In one or more embodiments, the multilayer scan may include a plurality of superimposed images. For example, a plurality of images 136a to e captured at a first location x may be merged to produce a first combined image 140a. In one or more embodiments, the combined image may include an extended field of depth (EFOD) image. In one or more embodiments, a first plurality of images 138a to e may be compiled together using the image processing module of the imaging device 100 to create a first combined image 140a.

[0051] Continuing to refer to Figure 1B, the imaging device 100 can combine the first multiple images 136 by aligning their pixels relative to each other. For example, but not limited to, the processor 104 of the imaging device 100 can determine which pixels of each image 136a to e are associated and adjust each of the first multiple images 136a to e accordingly (for example, each pixel of images 136a to e may be aligned or subjected to similar processing such as saturation, brightness, white balance, or other values).

[0052] Continuing to refer to Figure 1B, the imaging device 100 may combine a first set of images 136 by image fusion. As used in this disclosure, “image fusion” is the process of collecting important information from multiple images into fewer, usually one, images. One method of image fusion includes multifocal image fusion. As used in this disclosure, “multifocal image fusion” is an image fusion process that combines input images having different depths of focus to create at least one output image comprising in-focus image data from the input images. According to some embodiments, multifocal image fusion may include selecting in-focus image data from multiple input images. The determination of in-focus image data may be performed pixel by pixel according to, for example, one or more image fusion metrics. Generally, image fusion can be classified according to two categories: transformation and spatial domain. Transformations commonly used in image fusion are the discrete cosine transform (DCT) and the multi-scale transform (MST). In some cases, image fusion may be based on the MST. Exemplary MST methods include the Laplacian pyramid transform, gradient pyramid-based transform, morphological pyramid transform and major transform, discrete wavelet transform, shift-invariant wavelet transform (SIDWT), and discrete cosine harmonic wavelet transform (DCHWT). In some cases, DCT-based methods may be more efficient than MST in terms of transmission and image archiving. In some cases, DCT may be used for images encoded with Joint Photographic Experts Group (JPEG). The JPEG system consists of an encoder-decoder pair. In the encoder, the image is divided into non-overlapping 8x8 blocks, and a DCT coefficient is calculated for each block. Since the quantization of DCT coefficients is an irreversible process, many of the small DCT coefficients are quantized to 0, which corresponds to high frequencies.DCT-based image fusion algorithms perform better when multifocal image fusion methods are applied to compressed regions. In some cases, DCT domain-based image fusion methods do not require sequential decoding and encoding operations. Exemplary DCT image fusion processes include DCT+Variance, DCT+Corr_Eng, DCT+EOL, and DCT+VOL. Image fusion methods may additionally or alternatively include processes in the spatial domain. Image fusion can use focus measurement determination and use, including variance, energy of image gradient (EOG), Tenenbaum's algorithm (Tenengrad), energy of Laplacian (EOL), sum-modified-Laplacian (SML), and spatial frequency (SF). Image fusion may include aggregating focused regions from multiple input images into an image. In some cases, image edges or boundaries between different regions of the focused input images may be handled differently. For example, in some cases, sections of an image at the boundary between two (or more) focused images may be treated as a weighted average of the values ​​from the two (or more) closest points within the focused image. In some cases, machine learning can be used to assist the image fusion process.

[0053] Continuing to refer to Figure 1B, the imaging device 100 can combine a first set of images 136 using a machine learning model, such as the machine learning model described in Figure 2. Given two or more images as input, a machine learning model, such as a convolutional neural network, a deep neural network, or any combination thereof, is trained to generate an integrated image. Two-image input can be iteratively performed using one or both of the two images from the previous output of the neural network.

[0054] Continuing to refer to Figure 1B, the imaging device 100 may include an image processing module. Where used in this disclosure, “image processing module” is a component designed to process digital images. For example, but not limited to, the image processing module may be configured to compile multiple images of a multilayer scan to create an integrated image. In one embodiment, the image processing module may include multiple software algorithms that can analyze, manipulate, or otherwise enhance images, such as, but not limited to, multiple image processing techniques described below. In another embodiment, the image processing module may include, but not limited to, one or more hardware components such as graphics processing units (GPUs) that can accelerate the processing of large numbers of images. In some cases, the image processing module may be implemented with one or more image processing libraries, such as, but not limited to, OpenCV, PIL / Pillow, or ImageMagick. The image processing module may include, contain, or be communicably connected to, the optical system 120, the processor 104, and / or memory 108.

[0055] Referring further to Figure 1B, the image processing module may be configured to receive images from the optical system 120. In an indefinite example, the image processing module may be configured to receive images by generating a first image capture parameter, sending a command to the optical system to image a first image of a plurality of images using the first image capture parameter, generating a second image capture parameter, sending a command to the optical system to image a second image of a plurality of images using the second image capture parameter, and receiving the first and second images from the optical system. In another indefinite example, the optical system may capture multiple images using the same image capture parameter. The image capture parameter can be generated in response to user input or the processor 104.

[0056] Referring further to Figure 1B, multiple images can be transmitted from the optical system 120 to the image processing module via any suitable electronic communication protocol, including, but not limited to, packet-based protocols such as the transfer control protocol-internet protocol (TCP-IP) and the file transfer protocol (FTP). Receiving images may include retrieving images from a data store containing images, as described below. For example, but not limited to, images may be retrieved using a query that specifies a timestamp that the images may need to match.

[0057] Referring further to Figure 1B, the image processing module may be configured to process images. In one embodiment, the image processing module may be configured to compress and / or encode images to reduce file size and storage requirements while maintaining essential visual information necessary for further processing steps, as described below. In one embodiment, compressing and / or encoding multiple images can facilitate faster transmission of images. In some cases, the image processing module may be configured to perform lossless compression on images, which can maintain the original image quality of the images. In non-limiting examples, the image processing module may, but is not limited, use one or more lossless compression algorithms such as Huffman coding, Lempel-Ziv-Welch (LZW), Run-Length Encoding (RLE), etc., to identify and remove image redundancy without loss of information. In such embodiments, compressing and / or encoding each image of multiple images may include converting the file format of each image to PNG, GIF, lossless JPEG2000, etc. In one embodiment, an image compressed via lossless compression can be completely reconstructed to its original form (e.g., original image resolution, dimensions, color representation, format, etc.). In other cases, the image processing module may be configured to perform lossy compression on multiple images, which may sacrifice some image quality to achieve a higher compression ratio. In non-limiting examples, the image processing module may utilize one or more lossy compression algorithms, such as the discrete cosine transform (DCT) of JPEG or the wavelet transform of JPEG2000, to discard less important information within the image, resulting in a smaller file size but a slight decrease in image quality. In such embodiments, compressing and / or encoding each image of multiple images may include converting the file format of each image to JPEG, WebP, lossy JPEG2000, etc.

[0058] Referring further to Figure 1B, in one embodiment, image processing may include determining the degree of quality of depiction of a target area in an image or a group of images. In one embodiment, the image processing module may determine the degree of blurring in an image. In an unrestricted example, the image processing module may perform blur detection by performing an approximation such as a Fourier transform or Fast Fourier transform (FFT) of the image and analyzing the low-frequency and high-frequency distribution in the frequency-domain depiction of the resulting image. For example, but not limited to, the number of high-frequency values ​​below a threshold level may indicate blurring. In another unrestricted example, blur detection may be performed by convolving the image, image channels, etc., with a Laplacian kernel. For example, but not limited to, this may generate a numerical score that reflects the number of abrupt changes in intensity shown in each image, such that a high score indicates sharpness and a low score indicates blurring. In some cases, blur detection may be performed using a gradient-based operator that measures the operator based on the gradient or first derivative of the image, based on the hypothesis that abrupt changes indicate sharp edges in the image and therefore indicate a lower degree of blurring. In some cases, blur detection can be performed using wavelet-based operators that leverage the ability of discrete wavelet transform coefficients to describe the frequency and spatial content of an image. In other cases, blur detection can be performed using statistics-based operators that utilize several image statistics as texture descriptors to calculate the focus level. In other cases, blur detection can be performed by using discrete cosine transform (DCT) coefficients to calculate the focus level of an image from its frequency content. Additionally or alternatively, the image processing module may be configured to rank images according to the quality of depiction of the area in question and select the highest-ranked image from multiple images.

[0059] Referring further to Figure 1B, image processing may include highlighting at least one area of ​​interest through a plurality of image processing techniques to improve the quality (or degree of depiction) of the image for better processing and analysis, as further described in this disclosure. In one embodiment, the image processing module may be configured to perform a noise reduction operation on the image, which can remove or minimize noise (resulting from various causes such as sensor limitations, insufficient lighting conditions, and image compression), resulting in a cleaner, more visually coherent image. In some cases, the noise reduction operation may be performed using one or more image filters. For example, but not limited to, the noise reduction operation may include Gaussian filtering, median filtering, bilateral filtering, and the like. The noise reduction operation may be performed by the image processing module by averaging or removing neighboring pixel values ​​of each pixel in the image to reduce random fluctuations.

[0060] Referring further to Figure 1B, in another embodiment, the image processing module may be configured to perform a contrast enhancement operation on the image. In some cases, an image may exhibit low contrast, which can make it difficult, for example, to distinguish features from the background. A contrast enhancement operation can improve the contrast of an image by stretching the intensity range of the image and / or redistributing intensity values ​​(i.e., the degree of brightness of pixels in the image). In a non-limiting example, the intensity value can represent the tonal level or color of each pixel, ranging from 0 to 255 in the intensity range for an 8-bit image and from 0 to 16,777,215 for a 24-bit color image. In some cases, the contrast enhancement operation may include, but is not limited to, histogram equalization, adaptive histogram equalization (CLAHE), contrast stretching, etc. The image processing module may be configured to adjust the brightness levels in the image to make features more distinguishable (i.e., to improve the degree of depiction quality). Additionally or alternatively, the image processing module may be configured to perform a luminance normalization operation to compensate for variations in lighting conditions (i.e., non-uniform luminance levels). In some cases, the image may contain consistent luminance levels across the region after the luminance normalization operation performed by the image processing module. In a non-limiting example, the image processing module may perform global or local average normalization to calculate an average intensity value for the entire image or a region of the image, which can then be used to adjust the luminance levels.

[0061] Referring further to Figure 1B, in other embodiments, the image processing module may be configured to perform color space conversion operations to enhance the quality of the depiction. In a non-limiting example, for a color image (i.e., an RGB image), the image processing module may be configured to convert the RGB image to grayscale or HSV color space. Such a conversion can emphasize the difference in intensity values ​​between the area or feature of interest and the background. The image processing module may be further configured to perform image sharpening operations, such as unsharp masking, Laplacian sharpening, and high-pass filtering. The image processing module can use image sharpening operations to emphasize edges and details with respect to the area or feature of interest in the image by emphasizing the high-frequency components in the image.

[0062] Referring further to Figure 1B, processing an image may include separating a target region or feature from the rest of the image, depending on multiple image processing techniques. The image may include the highest-ranked image selected by the image processing module as described above. In one embodiment, the multiple image processing techniques may include one or more morphological operations, which are techniques developed based on set theory, lattice theory, topology, and random functions used to process geometric structures using structured elements. For the purposes of this disclosure, “structured elements” are small matrices or kernels that define the shape and size of the morphological operation. In some cases, the structured elements may be centered on each pixel of the image and used to determine the output pixel value of that location. In an unrestricted example, separating a target region or feature from an image may include applying a dilation operation, which is a basic morphological operation configured to expand or grow the boundaries of objects (e.g., cells, dust particles, etc.) in the image. In another unrestricted example, separating a target region or feature from an image may include applying a shrunk operation, which is a basic morphological operation configured to shrink or reduce the boundaries of objects in the image. In another non-limiting example, separating a target region or feature from an image may include applying an opening operation, a basic morphological operation configured to remove small objects or thin structures from an image while preserving larger structures. In yet another non-limiting example, separating a target region or feature from an image may include applying a closing operation, a basic morphological operation configured to fill in small gaps or holes in objects within an image while preserving the overall shape and size of the objects. These morphological operations may be performed by an image processing module to enhance the edges of objects, remove noise, or fill in gaps in the target region or feature before further processing.

[0063] Referring further to Figure 1B, in one embodiment, separating a target region or feature from an image may involve utilizing an edge detection technique that can detect one or more shapes defined by edges. As used in this disclosure, “edge detection technique” includes a mathematical method for identifying points in a digital image where the brightness of the image changes abruptly and / or where there are discontinuities. In one embodiment, such points may be organized into straight and / or curved segments that can be called “edges.” The edge detection technique may be performed by an image processing module using any suitable edge detection algorithm, including, but not limited to, Canny edge detection, Sobel operator edge detection, Prewitt operator edge detection, Laplacian operator edge detection, and / or difference edge detection. The edge detection technique may include phase congruence-based edge detection, which finds all locations in the image where all sine waves in the frequency domain, generated, for example using Fourier decomposition, may have matching phases that indicate the location of an edge. The edge detection technique can be used to detect the shape of a target feature, such as a cell, indicating a cell membrane or wall. In one embodiment, the edge detection technique can be used to find closed shapes formed by edges.

[0064] Referring further to Figure 1B, in a non-limiting example, separating a target feature from an image may include determining the target feature via edge detection techniques. The target feature may include a specific area within a digital image containing information relevant to further processing, as described below. In a non-limiting example, image data located outside the target feature may contain irrelevant or unwanted information. Such portions of the image containing irrelevant or unwanted information may be ignored by the image processing module, thereby allowing resources to be focused on the target feature. In some cases, the target feature may differ in size, shape, and / or location within the image. In a non-limiting example, the target feature may be presented as a circle around a cell nucleus. In some cases, the target feature may specify one or more coordinates, distances, etc., such as the center and radius of a circle around a cell nucleus in the image. The image processing module may then be configured to separate the target feature from the image based on the target feature. In a non-limiting example, the image processing module may crop the image according to a bounding box around the target feature.

[0065] Referring further to Figure 1B, the image processing module may be configured to perform connected component analysis (CCA) on the image for the separation of the feature of interest. “Connected component analysis (CCA),” also known as connected component labeling as used in this disclosure, is an image processing technique used to identify and label connected regions within a binary image (i.e., an image where each pixel has only two possible values: 0 or 1, black or white, or foreground and background). “Connected region” as used herein is a group of adjacent pixels that share the same value and are connected based on a predetermined neighborhood system, such as a 4-connected or 8-connected neighborhood. In some cases, the image processing module may convert the image to a binary image via a thresholding process, which may include setting a threshold to separate pixels in the image corresponding to the feature of interest (foreground) from pixels corresponding to the background. Pixels with intensity values ​​above the threshold may be set to 1 (white), and pixels below the threshold may be set to 0 (black). In one embodiment, the feature of interest can be detected and extracted by using CCA to identify multiple connected regions that exhibit specific properties or characteristics of the feature of interest. Next, the image processing module can filter multiple connected regions by analyzing multiple connected region properties, such as area, aspect ratio, height, width, and perimeter, but is not limited to these properties. In a non-limiting example, connected components that closely resemble the dimensions and aspect ratio of the target feature may be retained by the image processing module as the target feature, while other components may be discarded. The image processing module can be further configured to extract the target feature from the image for further processing, as described below.

[0066] Referring further to Figure 1B, in one embodiment, separating a target feature from an image may include segmenting the region depicting the target feature into a plurality of sub-regions. Segmenting a region into sub-regions may include segmenting the region according to the target feature and / or CCA via an image segmentation process. As used in this disclosure, “image segmentation process” is a process for dividing a digital image into one or more segments, each segment representing a distinct part of the image. The image segmentation process can change the representation of the image. The image segmentation process may be performed by an image processing module. In a non-limiting example, the image processing module may perform region-based segmentation, which includes growing regions from one or more seed points or pixels on an image based on similarity criteria. Similarity criteria may include, but are not limited to, color, intensity, texture, etc. In a non-limiting example, region-based segmentation may include region growth, region merging, watershed algorithms, etc.

[0067] Referring further to Figure 1B, in some embodiments, the imaging device 100 can remove artifacts identified by the machine vision system or optical character recognition system described above. Non-limiting examples of artifacts that can be removed include dust particles, bubbles, cracks in the slide 116, writing on the slide 116, shadows, and visual noise such as granular images. In some embodiments, artifacts can be partially removed and / or their visibility can be reduced.

[0068] Referring further to Figure 1B, in some embodiments, artifacts can be removed using artifact removal machine learning modes. In some embodiments, the artifact removal machine learning model can be trained on a dataset containing images associated with artifact-free images. In some embodiments, the artifact removal machine learning model can accept images containing artifacts as input and output images without artifacts. For example, the artifact removal machine learning model can accept images containing bubbles in a slide as input and output images without bubbles. In some embodiments, the artifact removal machine learning model can include generative machine learning models such as diffusion models. Diffusion models can learn the structure of a dataset by modeling how data points diffuse through latent space. In some embodiments, artifact removal can be performed locally. For example, but not limited to, the imaging device 100 can include a pre-trained artifact removal machine learning model and apply the model to images. In some embodiments, artifact removal can be performed externally. For example, the imaging device 100 can send image data to another computing device and receive artifact-removed images. In some embodiments, artifacts can be removed in real time. In some embodiments, artifacts can be removed based on user identification. For example, a user can use the mouse cursor to drag a box around an artifact, and the imaging device 100 can remove the artifact within the box.

[0069] Referring further to Figure 1B, in some embodiments, the imaging device 100 can remove artifacts from the first integrated image 140a or from any of the first multiple images before compilation. For example, in some embodiments, the imaging device 100 can remove artifacts from the second image 136b. For example, in some embodiments, the imaging device 100 can remove artifacts from a hybrid image such as the first integrated image 140a.

[0070] Referring further to Figure 1B, in one or more embodiments, the imaging device 100 may be configured to move the slide port 144, slide 116, one or more components of the optical system 120, etc., to a second position, where the second location x' of the specimen is within the line of sight 132 of the optical system 120. The second position may be based on one or more parameters, such as the original or updated parameter set of the first set of images. The new position may allow the new location to be placed within the FOV 156 of the optical component so that different sets of images from previous sets of images (e.g., z-stack) can be captured by the imaging system. The second position may include, for example, modifying the current position of the optical system 120 relative to slide 116 and specimen 112 (e.g., the first position) or vice versa, depending on the parameter set, as indicated by the directional arrows in Figure 1B, but not limited to. For example, but not limited to, the first location x may include a first target area 128a located at (x, y), and the second location x' may include a second target area 128b located at (x', y'). The second location x' may differ from the first location x, for example, by the variance of the x and / or y coordinate values, and may be located within the line of sight 132 by the transition from the first position to the second position. For example, but not limited to, the parameter set may indicate that the second position is achieved by moving the slide 116 in a particular direction, such as along the x axis, for example by 5 mm. Similarly, in another example, but not limited to, the second position can be found by correcting the original position of the optical system by 5 mm in the opposite direction. In some embodiments, such movement can be performed using an actuator mechanism 124. In some embodiments, the actuator mechanism 124 can move the slide port 144 so that the slide 116 is in a predetermined position relative to at least one optical system 120 so that the optical sensor 120 can capture an image as indicated by the parameter set.For example, but not limited to, the slide 116 can be placed on the slide port 144, and the slide 116 can be moved by moving the slide port 144. In some embodiments, the actuator mechanism 124 can move the slide 116 so that it is in a predetermined position relative to at least one optical system 120 so that the optical sensor 120 can capture an image as instructed by a parameter set. For example, the slide 116 may be connected to the actuator mechanism 124 so that the actuator mechanism 124 can move the slide 116 relative to at least one optical system 120. In some embodiments, the actuator mechanism 124 can move at least one optical system 120 so that the slide 116 is in a predetermined position relative to the slide 116 so that the optical system 120 can capture an image as instructed by a parameter set. For example, the slide 116 may be stationary, and the actuator mechanism 124 may move at least one optical system 120 to a predetermined position relative to the slide 116. In some embodiments, the actuator mechanism 124 can move several of the slide port 144, slide 116, and at least one optical system 120 so that they are in the correct relative positions. In some embodiments, the actuator mechanism 124 can move the slide port 144, slide 116, and / or at least one optical system 120 in real time. For example, but not limited to, user input of a parameter set can trigger substantially immediate movement of an item by the actuator mechanism 124.

[0071] Referring further to Figure 1B, in some embodiments, the imaging device 100 can capture a second plurality of images 144b of a second location x' of the specimen 112. In some embodiments, the imaging device 100 can capture a second plurality of images 144 using an optical system 120. In some embodiments, the second plurality of images 144 may include images of a second region of the specimen 112, such as a second target area 128b. In some embodiments, the second plurality of images 144 may include images of a region of the area captured in the first plurality of images 144. For example, but not limited to, the second plurality of images 144 may include a more magnified (i.e., different zoom levels), higher resolution per unit area image of a region in the first plurality of images. This allows the second plurality of images 144 to be displayed so that the user can detect smaller details within the imaged region. As described above, the second plurality of images 144 may include x and y coordinate shifts relative to the first plurality of images 126. For example, but not limited to, the second set of images 144 may partially overlap with the first set of images 136. In some embodiments, the second set of images 144 may have the same depth of field as the first set of images 136. In some embodiments, the second set of images 144 may have a different depth of field than the first set of images 136. In some embodiments, the second set of images 144 may have the same amount of layers or images as the first set of images 136.

[0072] Referring further to Figure 1B, each of the second set of images 144a-e may be captured at different depths of focus. For example, but not limited to, if the area of ​​interest in the first image 144a is out of focus, the second image 144b can be captured using a depth of focus set in the autofocus system and / or parameter set, and the second image will be in focus in the area of ​​interest. In an indefinite example, capturing the second image 144b may include detecting a specific imaging point of the specimen and / or the height of the specimen 112 in the second area of ​​interest 128b, and setting the focal height of at least one optical system 120 based on the height (e.g., thickness t) in the second area of ​​interest.

[0073] Referring further to Figure 1B, in some embodiments, the imaging device 100 can capture a second set of images 144 by an operator manipulating the input interface of the imaging device 100 to create a second set of parameters, and then the actuator mechanism 124 can initiate the movement of the slide 116 relative to the optical system 120 almost immediately after the input interface is manipulated, and then the optical system 120 can capture the second set of images 144 almost immediately after the actuator mechanism 124 completes its movement. The real-time movement of the actuator mechanism 124 and image capture allow the user to select a specific area of ​​interest and receive a high-resolution image of that area in real time without having to wait for a detailed image of the entire slide 116 to be captured. In some embodiments, artifacts can also be removed in real time. In some embodiments, it is also possible to annotate the image in real time. In non-limiting examples, the user can use the mouse cursor to hover over any location in the image and scroll the mouse wheel to indicate that the user wants to zoom in on that location. In this example, the x and y coordinates of the parameter set may be determined based on the cursor position, and the zoom level of the parameter set may be determined based on the amount the user scrolls the mouse wheel. This process may be useful, for example, when a user is looking at an image on slide 116, finds a target area, and wants to zoom in on that area more than the image resolution would normally allow. Using this process, the user can adjust the parameter set to zoom in, and the imaging device 100 can capture multiple images of higher resolution, compile the images, and display the combined image. This may be preferable to manually repeating the process of loading slide 116, setting the parameters (to higher resolution and / or zoom), and waiting for the device to generate and clean up the images.

[0074] Referring further to Figure 1B, in one or more embodiments, the imaging device 100 may be configured to compile a second plurality of images 144 into a second integrated image 140b. For example, but not limited to, a second plurality of images 144 captured at a second location x' of sample 112 may be merged to produce a second integrated image 140b. In one or more embodiments, the second plurality of images 144 may be compiled together to create a second integrated image 140b using an image processing module as described above in this disclosure. As will be understood by those skilled in the art, any number of plurality of images, and therefore any number of integrated images, may be captured by the imaging device 100. For example, a third plurality of images 148 having one or more images 148a~e may be captured based on the original parameter set or an updated parameter set. The third set of images may include images 148a to e of the third location x" of the specimen 112 after the optical system 120 and / or slide 116 has been moved to the third position in any of the methods described above in this disclosure. The third set of images can then be compiled by an imaging device such as a processor 104 to create a third integrated image 140c of the specimen 112 at location x".

[0075] Referring further to Figure 1B, in some embodiments, the imaging device 100 displays an integrated image, multiple images, or any individual images to the operator. In some embodiments, the first, second, or third integrated images 140a-c may be displayed to the user using an output device (e.g., a remote device or the display of the imaging device 100). Any of the integrated images, such as integrated images 140a-c, can be displayed to the user or operator in real time. In some embodiments, the first or second integrated image may be displayed to the user using an output interface. For example, any image described herein, but not limited to, can be displayed to the user on a display such as a screen. In some embodiments, any image described herein may be displayed to the user in the context of a graphical user interface (GUI). For example, the GUI may include controls for navigating the image, such as controls for zooming in or zooming out or changing the viewing location. The GUI may include a touchscreen. In some embodiments, displaying a first or second combined image to a user may include replacing areas of a first image in a group of images with a second image in a group of images in order to create a hybrid image (i.e., a combined image), and then displaying the hybrid image to the user. As used herein, “hybrid image” is an image constructed by combining a first image and a second image. In some embodiments, the creation of such a hybrid image may involve saving the second image. For example, but not limited to, if the second image covers a smaller area with a higher resolution per unit area than the first image, the second image may replace segments of the first area with a lower resolution per unit area that correspond to the area covered by the second image. In some embodiments, image adjustments may be made to compensate for visual differences between the first and second images at the boundary between the first and second images in the hybrid image.In a non-limiting example, the background color of an image may be adjusted to match the boundaries of the image. In another non-limiting example, the brightness of an image may be adjusted so that there is no significant difference in brightness between images. In some embodiments, artifacts may be removed from the second image and / or hybrid image as described above. In some embodiments, the second image may be displayed to the user in real time. For example, adjustments (such as annotation and / or artifact removal) may begin almost immediately after the second image is captured, and the adjusted version of the second image may be displayed to the user almost immediately after the adjustments are made. In some embodiments, an unadjusted version of the second image may be displayed to the user while the adjustments are being made. In some embodiments, if there are multiple images covering a particular area, a lower-resolution image of that area may be displayed when the user zooms out using the user interface.

[0076] Referring further to Figure 1B, in some embodiments, the imaging device 100 can transmit a data structure including a first image, a second image, a hybrid image, and / or multiple images to an external device. Such an external device may include, in non-limiting examples, a telephone, a tablet, or a computer. In some embodiments, such a transmission can configure the external device to display the image.

[0077] Referring further to Figure 1B, the imaging device 100 is configured, for quality assurance purposes, to compare the first integrated image 140a, the second integrated image 140b, and / or the third integrated image 140c with a goodness-of-fit measure by a processor 104 or the like. For example, the comparison may include comparing the first integrated image 140a with a first goodness-of-fit measure and the second integrated image 140b with a second goodness-of-fit measure. In some embodiments, each integrated image may be compared with a goodness-of-fit measure. For the purposes of this disclosure, “goodness-of-fit measure” is a standard or threshold for imaging quality. The goodness-of-fit measure may be based on a degree of quality, may include a degree of quality, and / or may be compared with a degree of quality. For example, but not limited to, if one of the integrated images does not meet the standard or threshold, the integrated image may be marked or flagged by the processor 104 for correction. For example, if the clarity level of a given integrated image is outside a predetermined threshold (e.g., range or limit) for quality such as clarity, the integrated image can be flagged, and the processor 104 can iteratively capture multiple updated images until an optimized version of the integrated image is captured. Quality can include image quality levels such as clarity, sharpness, focus, resolution, alignment, granularity, color, and value. For example, a location can be revisited so that an updated multilayer scan can be acquired at the location of sample 112 so that an adjusted and optimized integrated image can be compiled. The corresponding parameter set may also be adjusted to optimize the updated integrated image. In one or more embodiments, the processor 104 may be configured to generate a goodness-of-fit machine learning model. As used in this disclosure, “goodness-of-fit machine learning model” is a machine learning model configured to compare the quality of an image to a standard. The goodness-of-fit machine learning model may be the same as the machine learning model described later in Figure 2. Locations that need to be revisited may be stored in a data structure, as described below.

[0078] Continuing to refer to Figure 1B, in one or more embodiments, the data described herein may be represented as a data structure. For example, but not limited to, the data may include image data such as a first image data, a second image data, an integrated image, a merged image, etc. In some embodiments, the data structure may include one or more functions and / or variables, like a class in object-oriented programming. In some embodiments, the data structure may include data in the form of Boolean, integer, floating, string, date, etc. In an unrestricted example, the annotation data structure may include a string value representing the text of the annotation. In some embodiments, the data within the data structure may be organized into linked lists, trees, arrays, matrices, tensors, etc. In an unrestricted example, the annotation data structure may be organized into an array. In some embodiments, the data structure may include or be associated with one or more elements of metadata. The data structure may include one or more self-referential data elements that the processor 104 can use when interpreting the data structure. In an unrestricted example, the data structure may include a " <date> "and"< / date> It can include the tag ". Another non-exclusive example is a data structure that, as mentioned above, indicates that the tagged content needs correction. <flag> "and"< / flag> The tag may be included. Once the optimized integrated image is created, it can be compared again to the goodness-of-fit measure. Such a process may be repeated until the updated shape meets the quality standards. The task is marked as complete and the flag is removed from the integrated image.

[0079] Referring further to Figure 1B, the data structure may be stored, for example, in memory 108 or in a database. The database may be implemented as, but is not limited to, a relational database, a key-value lookup database such as a NoSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as appropriate upon reviewing the entire disclosure. The database may also be implemented using distributed data storage protocols and / or data structures such as distributed hash tables, alternatively or additionally. The database may contain multiple data entries and / or records as described above. Data entries in the database may be flagged with or linked to one or more additional elements of information, and one or more additional elements of information may be reflected in linked tables, such as tables related by data entry cells and / or indexes in a relational database. A person skilled in the art, upon reviewing the entire disclosure, will recognize a variety of ways in which data entries in a database can store, retrieve, organize, and / or reflect the data and / or records used herein, as well as categories and / or collections of data consistent with the disclosure.

[0080] Referring further to Figure 1B, in some embodiments, the data structure may be read and / or manipulated by the processor 104. In a non-limiting example, the image data structure may be read and displayed to the user. In another non-limiting example, the image data structure may be modified to remove artifacts, as described above.

[0081] Referring further to Figure 1B, in some embodiments the data structure may be calibrated. In some embodiments, the data structure may be trained using a machine learning algorithm. In a non-limiting example, the data structure may include an array of data representing the bias of the neural network's connections. In this example, the neural network can be trained with a set of training data, and the data in the array can be corrected using a backpropagation algorithm. Machine learning models and neural networks are described further herein.

[0082] Referring further to Figure 1B, the processor may be configured to use a naive Bayes classification algorithm to generate machine learning models, such as good-fit machine learning models. The naive Bayes classification algorithm generates classifiers by assigning class labels to problem instances, which are represented as vectors of element values. The class labels are drawn from a finite set. The naive Bayes classification algorithm may include generating a family of algorithms that, given class variables, assume that the values ​​of certain elements are independent of the values ​​of any other elements. The naive Bayes classification algorithm can be based on Bayes' theorem, expressed as P(A / B) = P(B / A)P(A)÷P(B), where P(A / B) is the probability of hypothesis A given data B, also known as the posterior probability; P(B / A) is the probability of data B given that hypothesis A is true; P(A) is the probability that hypothesis A is true regardless of the data, also known as the prior probability of A; and P(B) is the probability of data unrelated to the hypothesis. The naive Bayes algorithm can be generated by first converting the training data into a frequency table. The processor 104 can then compute a likelihood table by calculating the probabilities of different data entries and classification labels. The processor 104 can then use the naive Bayes equation to compute the posterior probability of each class. The class with the highest posterior probability is the prediction result. The naive Bayes classification algorithm can include a Gaussian model following a normal distribution. The naive Bayes classification algorithm can include a multinomial model used for discrete counts. The naive Bayes classification algorithm can include a Bernoulli model, which can be used when the vectors are binary.

[0083] Referring further to Figure 1B, the processor 104 may be configured to generate machine learning models, such as goodness-of-fit machine learning models, using the K-nearest neighbors (KNN) algorithm. Where used in this disclosure, the “K-nearest neighbors algorithm” includes a classification method that utilizes feature similarity to analyze how similar out-of-sample features are to the training data and classifies the input data into one or more clusters and / or feature categories as represented in the training data. This can be done by representing both the training data and the input data in vector form, using one or more measures of vector similarity to identify classifications in the training data and determine the classification of the input data. The K-nearest neighbors algorithm may include specifying a K value, which is a numerical value that instructs a classifier to select the training data of the k entries most similar to a given sample, determining the most common classifier for the entries in the database, and classifying the known sample. This can be done recursively and / or iteratively to generate classifiers that can be used to classify the input data as further samples. For example, an initial set of samples may be run to cover an initial heuristic and / or “first guess” in the output and / or relationships, which may be seeded with expert input received in accordance with any process described herein, but not limited to such processes. As a non-limiting example, the initial heuristic may include ranking the associations between input and training data elements. The heuristic may include selecting some of the highest-ranking associations and / or training data elements.

[0084] Continuing to refer to Figure 1B, the K-nearest neighbor algorithm generates a first vector output containing the data entry cluster, a second vector output containing the input data, and the distance between the first and second vector outputs can be calculated using any appropriate norm, such as cosine similarity or Euclidean distance measure. Each vector output can be represented as an n-tuple of values, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, the values ​​can be represented as an axis for each category of values ​​represented in the n-tuple of values, for the purposes of this disclosure, for the purposes of this disclosure, for the purposes of this disclosure, for the data entry cluster containing Vectors may be more similar if their directions are more similar, or more different if their directions are more divergent. However, vector similarity may alternatively or additionally be determined using the average of similarities between similar attributes, or any other measure of similarity suitable for the values ​​of any n-tuple, or an aggregation of numerical similarities for the purpose of a loss function, as described in more detail below. Any vectors described herein can be scaled so that each vector represents each attribute along a scale of equivalent values. Each vector may be “normalized,” or Pythagorean norm:

number

[0085] Referring here to Figure 1C, the imaging device 100 may be configured to combine a first integrated image 140a and a second integrated image 140b to create a combined image 152. For the purposes of this disclosure, “combined image” is a processed image composed of multiple integrated images. For example, the first integrated image 140a and the second integrated image 140b may each contain a partial image of the whole image of the specimen 112. Thus, the first integrated image 140a and the second integrated image 140b can be combined to include a whole image of at least a portion of the specimen 112. As will be understood by those skilled in the art, the exemplary embodiment includes first and second integrated images, but any number of integrated images can be used to create the combined image 152. For example, the combined image 152 may be created by combining, but not limited to, the first integrated image 140a, the second integrated image 140b, the third integrated image 140c, the fourth integrated image 140d, and so on. Similarly, any number of layers can be used to create an integrated image.

[0086] Referring further to Figure 1C, in one or more embodiments, the processor 104 may be configured to iteratively create and / or recreate the combined image until all locations on slide 116 have been imaged. In one or more embodiments, a modified combined image can be created, as previously described with respect to comparing the combined image to a goodness-of-fit measure. The iterations may include any number (e.g., two as described above). Any number of compiled combined images can then be combined to create a merged image.

[0087] Referring further to Figure 1C, the processor 104 may be configured to display any image described herein using the display device 144. As used herein, “display device” is a device used to display content. The display device 144 may include a user interface. As used herein, “user interface” is a means by which a user interacts with a computer system, for example, by using input devices and software. The user interface may include a graphical user interface (GUI), a command line interface (CLI), a menu-driven user interface, a touch user interface, a voice user interface (VUI), a form-based user interface, or any combination thereof. The user interface may include a smartphone, smart tablet, desktop, or laptop operated by the user. In one embodiment, the user interface may include a graphical user interface. As used herein, “graphical user interface (GUI)” is a graphical form of a user interface that enables a user to interact with an electronic device. In some embodiments, the GUI may include icons, menus, other visual indicators, or representations (graphics), audio indicators such as primary notation, as well as display information and associated user controls. A menu may include a list of choices from which the user can select one. A menu bar, such as a pull-down menu, may be displayed across the screen. When any option in this menu is clicked, a pull-down menu may appear. The menu may include a context menu that is displayed only when the user performs a specific action.One example of this is pressing the right mouse button. When this is done, a menu may appear under the cursor. Files, programs, web pages, etc., can be represented using small images within a graphical user interface. For example, links to the decentralized platform described in this disclosure may be incorporated using icons. Using icons can be a quick way to open documents, run programs, etc., because clicking them provides immediate access. Information contained in a user interface can be directly influenced using graphical control elements such as widgets. As used herein, “widget” is a user control element that allows a user to control and change the appearance of elements within a user interface. In this context, a widget can refer to a general GUI element such as a checkbox, a button, or a scroll bar to an instance of that element, or a customized set of such elements used for a particular function or application (such as a dialog box for a user to customize the appearance of a computer screen). User interface controls may include software components that the user interacts with through direct manipulation to read or edit information displayed through the user interface. Widgets may be used to display a list of related items, navigate the system using links and tabs, and manipulate data using checkboxes, radio buttons, etc.

[0088] Referring here to Figure 2, an exemplary embodiment of a machine learning module 200 capable of performing one or more machine learning processes described herein is shown. The machine learning module can use the machine learning processes to perform steps, methods, processes, etc., of decision, classification, and / or analysis described herein. Where used herein, “machine learning process” is a process that automatically uses training data 204 to generate an algorithm that is instantiated in hardware or software logic, data structures, and / or functions, which is executed by a computing device / module to produce an output 208 given data as input 212. This is in contrast to non-machine learning software programs, where the commands to be executed are predetermined by the user and written in a programming language. A machine learning process could include, for example, a goodness-of-fit machine learning process used to compare an integrated image to a predetermined threshold of a goodness-of-fit measure.

[0089] Referring further to Figure 2, the “training data” as used herein is data containing correlations that can be used by a machine learning process to model the relationships between two or more categories of data elements. For example, but not limited to, training data 204 may contain multiple data entries, also known as “training examples,” where each entry represents a set of data elements recorded, received, and / or generated together. Data elements may be correlated by the presence of common elements in a given data entry, proximity in a given data entry, etc. Multiple data entries within training data 204 may reveal one or more trends in the correlations between categories of data elements. For example, but not limited to, higher values ​​of a first data element belonging to a first category of data elements may correlate with higher values ​​of a second data element belonging to a second category of data elements, showing a possible proportional or other mathematical relationship linking values ​​belonging to the two categories. Multiple categories of data elements can be related to training data 204 according to various correlations. Correlation can indicate causal and / or predictive links between categories of data elements, which can be modeled as relationships, such as mathematical relationships, by machine learning processes, as will be described in more detail below. The training data 204 can be formatted and / or organized by categories of data elements, for example, by associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, the training data 204 may include data entered in a standardized format by a person or process, such that entries of a given data element in a given field in a form can be mapped to one or more descriptors of categories. Elements in the training data 204 may be linked to category descriptors by tags, tokens, or other data elements.For example, but not limited to, the training data 204 can be provided in a fixed-length format, a format that links the data location to categories such as comma-separated value (CSV) format, and / or a self-describing format such as an extensible markup language (XML) or JavaScript® Object Notation (JSON), enabling a process or device to detect the data categories.

[0090] Alternatively or additionally, continuing to refer to Figure 2, the training data 204 may include one or more unclassified elements. That is, the training data 204 may not be formatted or may not contain descriptors for some elements of the data. Machine learning algorithms and / or other processes can sort the training data 204 according to one or more classifications, for example, using natural language processing algorithms, tokenization, or the detection of correlation values ​​in the raw data. Categories can be generated using correlation and / or other processing algorithms. As a non-limiting example, in a corpus of text, phrases constituting a number "n" of compound words, such as nouns modified by other nouns, may be identified according to the statistically significant frequency of n-grams containing such words in a particular order. Such n-grams may be classified as linguistic elements, such as "words," which are tracked as well as single words, and new categories can be generated as a result of statistical analysis. Similarly, in data entries containing some text data, people's names are identified by referencing lists, dictionaries, or other glossaries of terms, enabling ad-hoc classification by machine learning algorithms and / or automated association of data within data entries with descriptors or given forms. The ability to automatically classify data entries makes the same training data 204 applicable to two or more different machine learning algorithms, as will be described in more detail below. The training data 204 used by the machine learning module 200 can correlate any input data described herein to any output data described herein. As a non-limiting exemplary example, the input may include a combined image and a goodness-of-fit measure, and the output may include a combined image identifier such as a flag or approval for the combined image.

[0091] Referring further to Figure 2, one or more supervised and / or unsupervised machine learning processes and / or models can be used to filter, sort, and / or select training data, as will be described in more detail below. Such models may include, but are not limited to, a training data classifier 216. The training data classifier 216 may include a “classifier,” which, as used in this disclosure, is defined as a mathematical model, a data structure that represents and / or uses a neural network, or a data structure that represents and / or uses a program generated by a machine learning algorithm known as a “classification algorithm,” which classifies an input into categories or bins of data and outputs categories or bins of data and / or labels associated therewith. The classifier may be configured to output at least one data point that labels or identifies datasets that have been clustered together and found to be close under a distance metric, as described below. The distance metric may include, but is not limited to, any norm, such as the Pythagorean norm. The machine learning module 200 can generate a classifier using a classification algorithm defined as the process by which a computing device and / or any module and / or component operating therein derives a classifier from the training data 204. Classification can be performed using, but are not limited to, linear classifiers such as logistic regression and / or naive Bayes classifiers, nearest neighbor classifiers such as K-nearest neighbor classifiers, support vector machines, least squares support vector machines, Fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learned vector quantization, and / or neural network-based classifiers. As a non-limiting example, the training data classifier 216 can classify elements of the training data into subcategories of quality, as described above. For example, but are not limited to, subcategories may include clarity, sharpness, color, granularity, resolution, focus, etc., relating to the quality of the integrated image.The subcategories of the combined image can be compared to a predetermined threshold of the goodness-of-fit measure, and the goodness-of-fit machine learning process can then determine whether the quality of the subcategories, etc., falls within or outside the predetermined threshold. If the quality of the combined image subcategories falls within the predetermined threshold, the combined image is assigned an identifier for an optimized combined image and can be used in combination with one or more optimized combined images to create a merged image. If the quality of the combined image subcategories falls outside the predetermined threshold, the combined image can be flagged (e.g., flagged), and then an updated combined image can be created. The updated combined image can then be compared to the goodness-of-fit measure. This process may be repeated until the updated combined image is identified as an item-specific combined image.

[0092] Referring further to Figure 2, training examples for use as training data may be selected from a population of potential examples according to a cohort related to the analytical problem to be solved, classification task, etc. Alternatively or additionally, training data may be selected to span a range of situations or inputs that the machine learning model and / or process may encounter during deployment. For example, for each category of input data to a machine learning process or model that may exist within a range of values ​​in a population of phenomena such as images, user data, process data, and physical data, the computing device, processor, and / or machine learning model may select training examples that represent each possible value and / or a representative sample of values ​​in such a range. The selection of representative samples may include, for example, selecting training examples in proportion to a statistically determined and / or predicted distribution of such values ​​according to relative frequency, such that values ​​that are encountered more frequently in the population of data thus analyzed are represented by more training examples than values ​​that are encountered less frequently. Alternatively or additionally, the set of training examples may be compared against and / or presented to the user a set of representative values ​​in a database so that the process can automatically or via user input detect one or more values ​​that are not included in the set of training examples. Computing devices, processors, and / or modules can automatically generate missing training examples. This can be done by receiving and / or acquiring missing input and / or output values, and correlating the missing input and / or output values ​​with the acquired values ​​and corresponding output and / or input values ​​that coexist in the data record, provided by the user and / or other devices, etc.

[0093] Referring further to Figure 2, the computer, processor, and / or module may be configured to sanitize the training data. When used in this disclosure, “sanitizing” the training data is the process of removing training examples that would hinder the convergence of the machine learning model and / or processing to useful results. For example, but not limited to, training examples may include input and / or output values ​​that are outliers from values ​​that are normally encountered, and a machine learning algorithm using training examples will adapt to quantities that are less likely to be input and / or output. For example, values ​​that exceed a threshold of standard deviation from the mean, median, or expected value may be removed. Alternatively or additionally, one or more training examples may be identified as having low-quality data, “low-quality” is defined as having a signal-to-noise ratio below a threshold.

[0094] As a non-limiting example, and referring further to Figure 2, images used to train an image classifier or other machine learning model, and / or images used in a process that takes images as input or generates images as output, may be rejected if their image quality falls below a threshold. For example, but not limited to, computing devices, processors, and / or modules may perform blur detection and eliminate one or more blurs. Blur detection can be performed, as a non-limiting example, by performing an approximation such as a Fourier transform or Fast Fourier transform (FFT) of the image and analyzing the distribution of low and high frequencies in the frequency domain depiction of the resulting image. The number of high-frequency values ​​below a threshold level may indicate blur. As a further non-limiting example, blur detection may be performed by convolving the image, the channels of the image, etc., with a Laplacian kernel. This can generate a numerical score that reflects the number of abrupt changes in intensity shown in the image, such that a high score indicates clarity and a low score indicates blur. Blur detection can be performed using gradient-based operators that measure the operator based on the gradient or first derivative of an image, based on the hypothesis that abrupt changes indicate sharp edges in an image and therefore indicate a lower degree of blur. Blur detection can be performed using wavelet-based operators that utilize the ability of discrete wavelet transform coefficients to describe the frequency and spatial content of an image. Blur detection can be performed using statistics-based operators that utilize several image statistics as texture descriptors to calculate the level of focus. Blur detection can be performed by using discrete cosine transform (DCT) coefficients to calculate the level of focus of an image from its frequency content.

[0095] Continuing to refer to Figure 2, the computing device, processor, and / or module may be configured to pre-tune one or more training examples. For example, if a machine learning model and / or process has one or more inputs and / or outputs that transmit or receive, requiring a certain number of bits, samples, or other data units, then the elements of one or more training examples used as inputs and / or outputs, or compared to them, can be modified to have such a number of data units. For example, the computing device, processor, and / or module can convert a smaller number of units, such as in a low-resolution image, into a desired number of units, for example, by upsampling and interpolation. As a non-limiting example, a low-resolution image may have 100 pixels, but the desired number of pixels may be 128. The processor can interpolate the low-resolution image to convert 100 pixels into 128 pixels. It should also be noted that those skilled in the art will know, upon reading this disclosure, various methods for interpolating a smaller number of data units, such as samples, pixels, or bits, into a desired number of such units. In some cases, the set of interpolation rules may be trained by a neural network or other machine learning model trained to predict interpolated pixel values ​​using training data, along with a set of very detailed inputs and / or outputs, and a corresponding set of inputs and / or outputs downsampled to fewer units. As a non-limiting example, sample inputs and / or outputs, such as a sample picture with sample augmented data units (e.g., pixels added between the original pixels), can be input to a neural network or machine learning model and output a pseudo-replica sample picture in which dummy values ​​are assigned to pixels between the original pixels based on the set of interpolation rules. As a non-limiting example, in the context of an image classifier, the machine learning model may have a set of interpolation rules trained on a set of very detailed images and images downsampled to fewer pixels, along with a neural network or other machine learning model trained using those examples to predict interpolated pixel values ​​in a face image context.As a result, an input having sample-expanded data units (with dummy values ​​added between the original data units) may be run through a trained neural network and / or model that can fill in values ​​to replace the dummy values. Alternatively or additionally, processors, computing devices, and / or modules may utilize sample expander methods, low-pass filters, or both. As used in this disclosure, a “low-pass filter” is a filter that allows signals below a selected cutoff frequency to pass through and attenuates signals above a cutoff frequency. The exact frequency response of the filter depends on the filter design. Computing devices, processors, and / or modules may use averaging, such as luma or chroma averaging in the image, to fill in data units between the original data units.

[0096] In some embodiments, continuing with reference to Figure 2, a computing device, processor, and / or module can downsample elements of a training example to a desired number of fewer data elements. As a non-limiting example, a high-resolution image may have 256 pixels, but the desired number of pixels could be 128. The processor can downsample the high-resolution image to convert 256 pixels to 128 pixels. In some embodiments, the processor may be configured to perform downsampling on the data. Downsampling, also known as decimation, can involve removing every Nth entry in a set of samples, all entries except the Nth, and so on, a process known as "compression," which can be performed, for example, by an N-sample compressor implemented using hardware or software. Anti-aliasing and / or anti-imaging filters and / or low-pass filters can be used to remove the side effects of compression.

[0097] Referring further to Figure 2, the machine learning module 200 may also be configured to run a lazy learning process 220 and / or protocol, which may alternatively be called a “lazy loading” or “invoke on demand” process and / or protocol, and may be a process in which machine learning is performed upon receiving an input that will be transformed into an output by combining the input and training set and deriving an algorithm used to generate an output on demand. For example, an initial set of simulations may be run to cover an initial heuristic and / or “first guess” on the output and / or relationships. As a non-limiting example, the initial heuristic may include ranking the associations between the input and elements of the training data 204. The heuristic may include selecting some of the highest-ranking associations and / or elements of the training data 204. Lazy learning can implement any suitable lazy learning algorithm, but is not limited to the K-nearest neighbor algorithm, the lazy naive Bayes algorithm, etc. Those skilled in the art will recognize, upon reviewing the entirety of this disclosure, a variety of lazy learning algorithms that can be applied to produce the outputs described herein, including, but not limited to, lazy learning applications of machine learning algorithms as described in more detail below.

[0098] Alternatively or additionally, referring again to Figure 2, a machine learning model 224 can be generated using the machine learning processes described in this disclosure. Where used in this disclosure, “machine learning model” is a data structure that represents and / or instantiates mathematical and / or algorithmic representations of relationships between inputs and outputs, generated and stored in memory using any machine learning process, including, but not limited to, any process such as those described above. Inputs are submitted to the generated machine learning model 224, and the machine learning model generates outputs based on the derived relationships. For example, a linear regression model generated using a linear regression algorithm may compute a linear combination of input data using coefficients derived during the machine learning process to compute output data. As a further non-limiting example, the machine learning model 224 may be generated by creating an artificial neural network, such as a convolutional neural network, having an input layer of nodes, one or more hidden layers, and an output layer of nodes. Connections between nodes can be created through a process of "training" the network, where elements from a set of 204 training data are applied to the input nodes, and then, using an appropriate training algorithm (e.g., Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms), the connections and weights between nodes in adjacent layers of the neural network are adjusted to produce the desired values ​​at the output nodes. This process is sometimes called deep learning.

[0099] Referring further to Figure 2, the machine learning algorithm may include at least one supervised machine learning process 228. The at least one supervised machine learning process 228 as defined herein includes an algorithm that receives a training set relating some inputs to some outputs and attempts to generate one or more data structures that represent and / or instantiate one or more mathematical relationships relating the inputs to the outputs, each of which is optimal according to some criteria specified for the algorithm using a scoring function. For example, the supervised learning algorithm may include the above-mentioned integrated image and goodness-of-fit measure as inputs and a scoring function that represents an identifier and the form of relationship to be detected between the inputs and outputs as outputs. The scoring function may, for example, attempt to maximize the probability that a given combination of inputs and / or element inputs is associated with a given output and minimize the probability that a given input is not associated with a given output. The scoring function can be expressed as a risk function representing the “expected loss” of the algorithm relating the input to the output, where the loss is calculated as an error function representing the degree to which the predictions generated by the relationship are inaccurate compared to a given input-output pair provided to the training data 204. Those skilled in the art will recognize, upon considering the entirety of this disclosure, various possible variations of at least one supervised machine learning process 228 that can be used to determine the relationship between inputs and outputs. The supervised machine learning process may include the classification algorithm defined above.

[0100] Referring further to Figure 2, training a supervised machine learning process may include, but are not limited to, iteratively updating coefficients, biases, and weights based on an error function, expected loss, and / or risk function. For example, the output generated by the supervised machine learning model using the input examples in the training examples may be compared to the output examples from the training examples. An error function can be generated based on the comparison, which may include any error function suitable for use in any machine learning algorithm described herein, such as squaring the difference between one or more sets of comparison values. Such an error function may be used to update one or more weights, biases, coefficients, or other parameters of the machine learning model via any suitable process, including, but are not limited to, a gradient descent process, a least-squares process, and / or other processes described herein. This may be done iteratively and / or recursively to progressively adjust such weights, biases, coefficients, or other parameters. The updates may be performed in a neural network using one or more backpropagation algorithms. The iterative and / or recursive updates of weights, biases, coefficients, or other parameters described above can be performed until the currently available training data is exhausted and / or until a convergence test is passed. A “convergence test” is a test of conditions selected to indicate that the model and / or its weights, biases, coefficients, or other parameters have reached a certain degree of accuracy. A convergence test can, for example, compare the difference between two or more consecutive error or error function values, and a difference below a threshold amount can be interpreted as indicating convergence. Alternatively or additionally, one or more error and / or error function values ​​evaluated in training iterations can be compared to a threshold.

[0101] Referring further to Figure 2, computing devices, processors, and / or modules may be configured to execute methods, process steps, series of process steps, and / or algorithms described with reference to this figure in any order and to any degree of repetition. For example, computing devices, processors, and / or modules may be configured to repeatedly execute a single step, a series of steps, and / or algorithm until a desired or instructed result is achieved. The repetition of a step or series of steps is performed iteratively and / or recursively using the output of the previous iteration as input to the subsequent iteration, aggregating the inputs and / or outputs of the iterations to produce an aggregated result, which may reduce or decrease one or more variables, such as global variables, and / or divide a larger processing task into a set of smaller processing tasks that are repeatedly dealt with. Computing devices, processors, and / or modules may execute any step, series of steps, or algorithm in parallel, such as executing a step simultaneously and / or substantially simultaneously multiple times using two or more parallel threads, processor cores, etc. Task division between parallel threads and / or processes may be performed according to any protocol suitable for task division between iterations. Those skilled in the art, upon reviewing the entirety of this disclosure, will recognize a variety of ways in which processes, sets of processes, processing tasks, and / or data can be subdivided, shared, or otherwise processed using iterative, recursive, and / or parallel processing.

[0102] Referring further to Figure 2, the machine learning process may include at least one unsupervised machine learning process 232. As used herein, an unsupervised machine learning process is a process that derives inferences within a dataset regardless of labels. As a result, an unsupervised machine learning process can freely discover any structures, relationships, and / or correlations provided within the data. An unsupervised process may not require a response variable. An unsupervised process can be used to find interesting patterns and / or inferences between variables, determine the degree of correlation between two or more variables, and so on.

[0103] Referring further to Figure 2, the machine learning module 200 can be designed and configured to create a machine learning model 224 using techniques for developing linear regression models. Linear regression models can include ordinary least squares regression, which aims to minimize the square of the difference between the predicted and actual results according to a suitable norm for measuring such differences (e.g., vector space distance norm). To improve minimization, the coefficients of the resulting linear equation can be modified. Linear regression models can include ridge regression, where the function to be minimized is a least squares function and a term that multiplies the square of each coefficient by a scalar quantity to penalize large coefficients. Linear regression models can include least absolute shrinkage and selection operator (LASSO) models, where ridge regression is combined with multiplying the least squares term by a coefficient obtained by dividing 1 by twice the number of samples. Linear regression models can include multitask LASSO models, where the norm applied to the least squares term of the LASSO model is the Frobenius norm, which corresponds to the square root of the sum of the squares of all terms. The linear regression model may include elastic net models, multitask elastic net models, minimum angle regression models, LARS LASSO models, orthogonal matching tracking models, Bayesian regression models, logistic regression models, stochastic gradient descent models, perceptron models, passive attack algorithms, robust regression models, Hoover regression models, or any other suitable model that a person skilled in the art may conceive of when considering the entire disclosure. In one embodiment, the linear regression model can be generalized to a polynomial regression model, thereby finding a polynomial (e.g., a quadratic, cubic, or higher-order equation) that provides the best predictive output / actual output fit. As will be apparent to a person skilled in the art when considering the entire disclosure, similar methods as described above can be applied to minimize the error function.

[0104] Continuing to refer to Figure 2, machine learning algorithms can include, but are not limited to, linear discriminant analysis. Machine learning algorithms can include quadratic discriminant analysis. Machine learning algorithms can include kernel ridge regression. Machine learning algorithms can include, but are not limited to, support vector machines, which include support vector classification-based regression processes. Machine learning algorithms can include stochastic gradient descent algorithms, which include classification and regression algorithms based on stochastic gradient descent. Machine learning algorithms can include nearest neighbor algorithms. Machine learning algorithms can include various forms of latent space regularization, such as variational regularization. Machine learning algorithms can include Gaussian processes, such as Gaussian process regression. Machine learning algorithms can include cross-decomposition algorithms, which include partial least squares and / or canonical correlation analysis. Machine learning algorithms can include naive Bayes methods. Machine learning algorithms can include decision tree-based algorithms, such as decision tree classification or regression algorithms. Machine learning algorithms can include ensemble methods, such as bagging meta-estimators, randomized tree forests, AdaBoost, gradient tree boosting, and / or voting classifier methods. Machine learning algorithms can include neural network algorithms, including convolutional neural network processes.

[0105] Referring further to Figure 2, machine learning models and / or processes can be deployed or instantiated by being incorporated into programs, devices, systems and / or modules. For example, but not limited to, machine learning models, neural networks, and / or some or all of their parameters can be stored and / or deployed in any memory or circuit configuration. Parameters such as coefficients, weights, and / or biases may be stored as circuit-based constants such as arrays of wires set to logical "1" and "0" voltage levels in a logic circuit and / or binary inputs and / or outputs to represent numbers in any suitable encoding system, including two's complement, or they may be stored in any volatile and / or non-volatile memory. Similarly, mathematical operations and inputs and / or outputs of data to and from models, neural network layers, etc., can be instantiated in the form of machine code such as instructions, binary arithmetic code instructions, assembly language, or any higher-order programming language within hardware circuit configurations and / or firmware. Machine learning processes and / or models can be instantiated using any technology for hardware and / or software instantiation of memory, instructions, data structures, and / or algorithms, which includes, but is not limited to, the manufacture and / or configuration of non-reconfigurable hardware elements, circuits, and / or modules such as ASICs, but is not limited to, the manufacture and / or configuration of reconfigurable hardware elements, circuits, and / or modules such as FPGAs, but is not limited to, the manufacture and / or configuration of non-reconfigurable and / or configured non-reconfigurable memory elements, circuits, and / or modules such as non-reconfigurable ROMs, but is not limited to, any combination of the manufacture and / or configuration of reconfigurable and / or reconfigurable memory elements, circuits, and / or any computing devices and / or components such as reconfigurable ROMs or other memory technologies described herein.Such deployments and / or instantiated machine learning models and / or algorithms can receive input from any other processes, modules, and / or components described in this disclosure and generate outputs to any other processes, modules, and / or components described in this disclosure.

[0106] Continuing to refer to Figure 2, any machine learning model and / or algorithm can be modified, improved, and / or enhanced after its initial deployment and / or instantiation by performing and / or repeating any process of training, retraining, deploying, and / or instantiation of that machine learning model and / or algorithm. Such retraining, deployment, and / or instantiation may be performed as a periodic or regular process, such as retraining, deployment, and / or instantiation over a periodic elapsed time, after some measure of quantity, such as the number of bytes of data processed or other measures, the number of uses or executions of the processes described herein, and / or according to a software, firmware, or other update schedule. Alternatively or additionally, retraining, deployment, and / or instantiation may be event-based, but not limited to, triggered by user input exhibiting suboptimal or other problematic performance, and / or by automated field testing and / or audit processes, and the output of the machine learning model and / or algorithm and / or its error and / or error function may be compared to any threshold, convergence test, etc., and / or the output of the processes described herein may be compared to similar threshold, convergence test, etc. Event-based retraining, deployment, and / or instantiation may, alternatively or additionally, be triggered by the reception and / or generation of one or more new training examples. Several new training examples can be compared to a pre-configured threshold, and if the threshold is exceeded, retraining, deployment, and / or instantiation can be triggered.

[0107] Referring further to Figure 2, retraining and / or additional training can be performed using any currently or previously deployed version of the machine learning model and / or algorithm as a starting point, and using any process for training described above. Training data for retraining can be collected, pre-adjusted, screened, classified, sanitized, or otherwise processed in accordance with any process described herein. The training data may include, but are not limited to, training examples that include inputs and correlated outputs used, received, and / or generated from any version of any system, module, machine learning model or algorithm, apparatus, and / or method described herein. Such examples may be modified and / or labeled in accordance with user feedback or other processes to show desired results, and / or may have actual or measured results from processes modeled and / or predicted by the system, module, machine learning model or algorithm, apparatus, and / or method as “desired” results compared to the output of the training process as described above.

[0108] Redeployment can be performed by any reconfiguration and / or rewriting of reconfigurable and / or rewritable circuit and / or memory elements. Alternatively, redeployment may be performed by generating new hardware and / or software components, circuits, instructions, etc., which may be added to and / or replace existing hardware and / or software components, circuits, instructions, etc.

[0109] Referring further to Figure 2, one or more of the processes or algorithms described above may be performed by at least one dedicated hardware unit 232. For the purposes of this figure, “dedicated hardware unit” is a hardware component, circuit, etc. other than the main control circuit and / or processor that performs the steps of the method described herein, but is not limited to, being specifically designated or selected to perform one or more particular tasks and / or processes described with reference to this figure, such as pre-conditioning and / or sanitizing training data, and / or training machine learning algorithms and / or models. The dedicated hardware unit 232 may include, but is not limited to, a hardware unit that can perform iterative or centralized computations, such as matrix-based computations for updating or adjusting parameters, weights, coefficients, and / or biases of machine learning models and / or neural networks, using pipelined, parallel processing, etc., efficiently. Such a hardware unit may be optimized for such processes by including, for example, a dedicated circuit configuration for matrix and / or signal processing operations, including multiple arithmetic and / or logic circuit units such as multipliers and / or adders that can operate simultaneously and / or in parallel. Such dedicated hardware units 232 may include, but are not limited to, a graphical processing unit (GPU), a dedicated signal processing module, an FPGA, or other reconfigurable hardware configured to instantiate parallel processing units for one or more specific tasks. A computing device, processor, apparatus, or module may be configured to instruct one or more dedicated hardware units 232 to perform one or more operations described herein, such as evaluating model and / or algorithm outputs, one-time or iterative updates to parameters, coefficients, weights, and / or biases, and / or any other operations such as vector and / or matrix operations described herein.

[0110] Referring here to Figure 3, an exemplary score database 300 is shown by a block diagram. In one embodiment, it can store any past or current version of the data disclosed herein, including user profiles 108, user labels 112, metadata 116, multiple entity categories, scanned user labels 120, named entities 130, confidence scores 134, etc. The processor 104 can be communicated with the score database 300. For example, in some cases, the database 300 may be local to the processor 104. Alternatively or additionally, in some cases, the database 300 may be remote to the processor 104 and may communicate with the processor 104 over one or more networks. The networks may include, but are not limited to, cloud networks, mesh networks, etc. For example, a “cloud-based” system, as the term is used herein, may refer to a system that includes software and / or data stored, managed, and / or processed on a network of remote servers hosted in the “cloud,” for example, over the Internet, rather than on a local server or personal computer. Where used in this disclosure, “mesh network” is a local network topology in which the infrastructure processor 104 connects directly, dynamically, and non-hierarchically to as many other computing devices as possible. Where used in this disclosure, “network topology” is the arrangement of elements of a communication network. The score database 300 may be implemented as a relational database, a key-value lookup database such as a NoSQL database, or any other format or structure for use as a database that a person skilled in the art would deem appropriate upon reviewing this entire disclosure. The score database 300 may also be implemented using a distributed data storage protocol and / or data structure such as a distributed hash table, alternatively or additionally. The score database 300 may contain multiple data entries and / or records as described above.A data entry in a database may be flagged by or linked to one or more additional elements of information, and one or more additional elements of information may be reflected in linked tables, such as tables, which are associated by data entry cells and / or indexes in a relational database. A person skilled in the art will, upon reviewing the entirety of this disclosure, recognize a variety of ways in which a data entry in a database may store, retrieve, organize, and / or reflect the data and / or records used herein, as well as categories and / or groups of data consistent with this disclosure.

[0111] Referring here to Figure 4, an exemplary embodiment of neural network 400 is shown. Also known as an artificial neural network, neural network 400 is a network of “nodes,” or a data structure having one or more inputs, one or more outputs, and a function that determines the output based on the input. Such nodes can be organized into a network, such as a convolutional neural network, which includes, but is not limited to, an input layer of node 404, one or more hidden layers 408, and an output layer of node 412. Connections between nodes can be created through a process of “training” the network, in which elements from a training dataset are applied to the input nodes, and then, using an appropriate training algorithm (e.g., Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms), the connections and weights between nodes in adjacent layers of the neural network are adjusted to produce desired values ​​in the output nodes. This process is sometimes called deep learning. Connections may be made only from input nodes to output nodes in a “feedforward” network, or the output of one layer may be fed back to the input of the same or different layers in a “recurrent network.” As a further non-limiting example, a neural network may comprise a convolutional neural network having an input layer for a node, one or more hidden layers, and an output layer for a node. As used in this disclosure, a “convolutional neural network” is a neural network having one or more additional layers, such as pooling layers and fully connected layers, along with at least one hidden layer, which is a convolutional layer that convolves the input to that layer with a subset of inputs known as a “kernel.”

[0112] Referring now to Figure 5, an exemplary embodiment of a neural network node is shown. A node may have multiple inputs x that can be inputs to the neural network containing the node and / or receive numerical values ​​from other nodes. i It can include the following: The node has each input x iThe weights multiplied by w i The inputs can be weighted using . Additionally or alternatively, a bias b may be added to the weighted inputs such that an offset is added to each unit in the neural network layer, independent of the inputs to the layer. The weighted sum can then be input to a function φ, which can generate one or more outputs y. Input x i The weight applied w i The weight w can indicate whether an input is "excitatory," for example, by having a large corresponding weight, indicating that the input strongly influences one or more outputs y, and / or "inhibitory," for example, by having a small corresponding weight, indicating that the input weakly influences another input y. i The value of can be determined by training a neural network using training data, and training can be performed using any appropriate process as described above.

[0113] Referring here to Figure 6, an exemplary embodiment of the fuzzy set comparison 600 is shown. In a non-limiting embodiment, the fuzzy set comparison 600 may coincide with the fuzzy set comparison in Figure 1A. In another non-limiting example, the fuzzy set comparison 600 may coincide with name / version matching as described herein. For example, but not limited to, the parameters, weights, and / or coefficients of the membership function may be adjusted using any machine learning method for name / version matching as described herein. In another non-limiting embodiment, the fuzzy set may represent scanned user labels 120 and historically scanned user labels from Figure 1A.

[0114] Alternatively or additionally, referring further to Figure 6, the fuzzy set comparison 600 may be generated as a function for determining a data compatibility threshold. The compatibility threshold may be determined by a computing device. In some embodiments, the computing device may determine the compatibility threshold and / or version certifier using a logic comparison program, such as a fuzzy logic model, though not limited to this. Each such compatibility threshold may be represented as a value of a post variable representing the compatibility threshold, or, in other words, as the above fuzzy set corresponding to a degree of compatibility and / or acceptableness calculated using statistical, machine learning, or other methods that a person skilled in the art could conceive of in considering the entire disclosure. In some embodiments, determining the compatibility threshold and / or version certifier may involve using a linear regression model. The linear regression model may include a machine learning model. The linear regression model may, but is not limited to this, map statistics, such as the frequency of version numbers within the same range, to the compatibility threshold and / or version certifier. In some embodiments, determining the compatibility threshold for a post may involve using a classification model. The classification model may, but is not limited to this, be input with data collected based on the frequency of occurrence of a range of version numbers, a language indicator of compatibility and / or acceptableness, etc., and be configured to cluster the data to centroids. The centroids can include the scores assigned to each compatibility threshold, so that a score can be assigned to each compatibility threshold. In some embodiments, the classification model may include a K-means clustering model. In some embodiments, the classification model may include a particle swarm optimization model. In some embodiments, determining the compatibility thresholds may include using a fuzzy inference engine. The fuzzy inference engine may be configured to map one or more compatibility thresholds using fuzzy logic. In some embodiments, multiple computing devices may be arranged in the compatibility configuration by a logic comparison program. As used in this disclosure, “compatibility configuration” is any grouping of objects and / or data based on skill levels and / or output scores.The membership function coefficients and / or constants described above can be adjusted according to classification and / or clustering algorithms. For example, a clustering algorithm can determine a Gaussian or other distribution of questions relating to the centroids corresponding to a given compatibility threshold and / or version authenticator, and using iteration or other methods, find a membership function of any of the above membership function types that minimizes the mean error from a statistically determined distribution, such as a triangle or Gaussian membership function relating to the centroids representing the center of the distribution that best matches the distribution. The error function to be minimized and / or the method of minimization can be performed without limitation according to the error function and / or error function minimization processes and / or methods described herein.

[0115] Referring further to Figure 6, the inference engine may be implemented according to the input of a plurality of scanned user labels 120 and a plurality of historically scanned user labels. For example, the acceptance variable may represent a first measurable value relating to the classification of the plurality of scanned user labels 120 into historically scanned user labels. Continuing this example, the output variable may represent a confidence score. In one embodiment, the plurality of scanned user labels 120 and / or historically scanned user labels may be represented by their own fuzzy sets. In other embodiments, the evaluation coefficient may be represented according to the intersection of two fuzzy sets, as shown in Figure 6, and the inference engine may combine its arbitrary rules such as semantic versioning, semantic language, and version range. The degree to which a given input function membership matches a given rule can be determined by the triangular norm or "T-norm" of the output function having a rule or input function that satisfies the requirements of commutativity (T(a,b)=T(b,a)), monotonicity (T(a,b)≦T(c,d) for a≦c and b≦d), (associativity: T(a,T(b,c))=T(T(a,b),c)), and that the number 1 functions as the identity element, such as min(a,b), the product of a and b, the drastic product of a and b, the Hamacher product of a and b. Combinations of rules (combinations of "logical AND" or "logical OR" of rule membership determination) can be performed using any T-conorm, as represented by the inverted T symbol, i.e., "⊥", such as max(a,b), the stochastic sum of a and b (a+ba*b), the bounded sum, and / or the drastic T-conorm. Any T-conorm may be used that satisfies the properties of commutativity: ⊥(a,b)=⊥(b,a), monotonicity: ⊥(a,b)≦⊥(c,d) if a≦c and b≦d, associativity: ⊥(a,⊥(b,c))=⊥(⊥(a,b),c), and identity element 0. Alternatively or additionally, the T-conorm may be approximated by sums, such as in a “product-sum” inference engine where the T-norm is a product and the T-conorm is a sum.The final output score or other fuzzy inference output can be determined from the output membership function described above using any appropriate defussing process, including, but not limited to, the mean of maximum defussing, the centroid of area / centroid defussing, central mean defussing, area bisector defussing, etc. Alternatively or additionally, the output rules may be replaced by a function from the Takagi-Sugeno-King (TSK) fuzzy model.

[0116] The first fuzzy set 604 can be represented according to a first membership function 608 that represents the probability that an input falling within a first range of values ​​612 is a member of the first fuzzy set 604, the first membership function 608 having a range of values ​​such as the interval [0,1], and the area below the first membership function 608 can represent the set of values ​​within the first fuzzy set 604. In this exemplary description, for clarity, the first range of values ​​612 is shown as a range on a single numerical line or axis, but the first range of values ​​612 may be defined in two or more dimensions, for example, representing the Cartesian product between multiple ranges, curves, axes, spaces, dimensions, etc. The first membership function 608 may include any suitable function that maps the first range 612 to a probability interval, including, but not limited to, a trigonometric function defined by two linear elements such as a line segment or plane intersecting at or below the top of the probability interval. As a non-restrictive example, triangular membership functions can be defined as follows:

number

[0117] The trapezoidal membership function can be defined as follows:

number

[0118] The sigmoid function can be defined as follows:

number

[0119] The Gaussian membership function can be defined as follows:

number

[0120] The bell membership function can be defined as follows:

number

[0121] Those skilled in the art will, upon reviewing the entirety of this disclosure, recognize a variety of alternative or additional membership functions that may be used in accordance with this disclosure.

[0122] The first fuzzy set 604 can represent any of the above values ​​or combinations of values, including any multiple scanned user labels 120 and historically scanned user labels. A second fuzzy set 616, which can represent any values ​​that can be represented by the first fuzzy set 604, may be defined by a second membership function 620 on a second range 624. The second range 624 may be identical to and / or overlap with the first range 612, and / or may be combined with the first range via a Cartesian product or the like to generate a mapping that allows evaluation of the overlap between the first fuzzy set 604 and the second fuzzy set 616. If the first fuzzy set 604 and the second fuzzy set 616 have an overlapping region 636, the first membership function 608 and the second membership function 620 may intersect at a point 632 that represents the probability of a match between the first fuzzy set 604 and the second fuzzy set 616, as defined on a probability interval. Alternatively or additionally, a single value in the first and / or second fuzzy set may be located at a locus 636 on the first range 612 and / or the second range 624, and the probability of membership may be taken by evaluating the first membership function 608 and / or the second membership function 620 at that range point. The probabilities at 628 and / or 632 can be compared to a threshold 640 to determine whether a positive match is indicated. In an unrestricted example, the threshold 640 can represent the degree of matching between the first fuzzy set 604 and the second fuzzy set 616, and / or the degree of matching between single values ​​within them or between any set, sufficient for the purposes of the matching process. For example, the goodness-of-fit measure may indicate a sufficient degree of overlap with the fuzzy sets representing the image as described above. Each threshold may be established by one or more user inputs. Alternatively or additionally, each threshold may be adjusted by machine learning and / or statistical processes, for example, not limitedly, but as described in more detail below.

[0123] Referring here to Figure 7, a flowchart of an exemplary method 700 for image generation of a sample is shown. In step 705, method 700 includes receiving a parameter set using processor 104. This can be carried out as described with reference to Figures 1 to 7.

[0124] In step 710, method 700 includes the processor capturing a first set of images of a first target area at a first location of the sample. This can be carried out as described with reference to Figures 1 to 7.

[0125] In step 715, method 700 includes compiling the first multiple layers into a first unified image using a processor. This can be carried out as described with reference to Figures 1 to 7.

[0126] In step 720, method 700 includes the processor capturing a second set of images of a second target area at a second location of the sample. This can be carried out as described with reference to Figures 1 to 7.

[0127] In step 725, method 700 includes compiling a second set of layers into a second integrated image. This can be carried out as described with reference to Figures 1 to 7.

[0128] In step 725, method 700 includes combining the first integrated image and the second integrated image into a combined image using a processor, and displaying the combined image using a processor. This can be carried out as described with reference to Figures 1 to 7.

[0129] In step 730, method 700 includes displaying the combined image. This can be carried out as described with reference to Figures 1 to 7.

[0130] It should be noted that any one or more of the embodiments and models described herein can be conveniently implemented using one or more machines programmed according to the teachings herein (e.g., one or more computing devices used as a user computing device for electronic documents, one or more server devices such as a document server, etc.), as will be obvious to those skilled in the art. As will be obvious to those skilled in the art, appropriate software coding can be readily produced by programmers skilled in the art based on the teachings of this disclosure. The above embodiments and implementations using software and / or software modules may also include appropriate hardware to assist in the implementation of machine-executable instructions of the software and / or software modules.

[0131] Such software may be a computer program product that uses a machine-readable storage medium. The machine-readable storage medium may be any medium capable of storing and / or encoding a set of instructions for execution by a machine (e.g., a computing device), causing the machine to execute any one of the methods and / or embodiments described herein. Examples of machine-readable storage mediums include, but are not limited to, magnetic disks, optical disks (e.g., CDs, CD-Rs, DVDs, DVD-Rs, etc.), magneto-optical disks, read-only memory "ROM" devices, random-access memory "RAM" devices, magnetic cards, optical cards, solid-state memory devices, EPROMs, EEPROMs, and any combination thereof. As used herein, machine-readable storage medium is intended to include a single medium, as well as a collection of physically separate media, such as a compact disk combined with computer memory or a collection of one or more hard disk drives. As used herein, machine-readable storage medium does not include transient forms of signal transmission.

[0132] Such software may also include information (e.g., data) that is carried as a data signal on a data carrier, such as a carrier wave. For example, machine-executable information may be included as a data-carrying signal embodied on a data carrier, where the signal encodes a set of instructions or a portion thereof for execution by a machine (e.g., a computing device), and any relevant information (e.g., data structures and data) that causes the machine to execute any one of the methods and / or embodiments described herein.

[0133] Examples of computing devices include, but are not limited to, e-book readers, computer workstations, terminal computers, server computers, handheld devices (e.g., tablet computers, smartphones, etc.), web appliances, network routers, network switches, network bridges, any machine capable of executing a set of instructions specifying actions to be taken by a machine, and any combination thereof. For example, a computing device may include and / or be included in a kiosk.

[0134] Figure 8 shows a schematic diagram of one embodiment of a computing device in an exemplary form of computer system 800, in which a set of instructions for causing a control system to execute one or more of the embodiments and / or methodologies of the present disclosure can be executed. It is also conceivable that multiple computing devices could be used to implement a specially configured set of instructions for causing one or more of the devices to execute any one or more of the embodiments and / or methodologies of the present disclosure. Computer system 800 includes a processor 804 and memory 808 that communicate with each other and with other components via a bus 812. Bus 812 may include any of several types of bus structures, including but not limited to memory buses, memory controllers, peripheral buses, local buses, and any combination thereof, using any of various bus architectures.

[0135] The processor 804 may include any suitable processor, such as a processor incorporating logic circuit configurations for performing arithmetic and logic operations, such as an arithmetic and logic unit (ALU), which is coordinated by a state machine and can be directed by operational inputs from memory and / or sensors. The processor 804 may, as an unspecified example, be organized according to the von Neumann architecture and / or the Harvard architecture. The processor 804 may include, incorporate, and / or be incorporated into, a microcontroller, microprocessor, digital signal processor (DSP), field-programmable gate array (FPGA), complex programmable logic device (CPLD), graphical processing unit (GPU), general-purpose GPU, tensor processing unit (TPU), analog or mixed-signal processor, trusted platform module (TPM), floating-point unit (FPU), and / or system-on-a-chip (SoC).

[0136] Memory 808 may include, but is not limited to, a variety of components (e.g., machine-readable media) including random-access memory components, read-only components, and any combination thereof. For example, a basic input / output system (BIOS) 816 containing basic routines that help transfer information between elements within the computer system 800, such as during startup, may be stored in memory 808. Memory 808 may also include instructions (e.g., software) 820 (e.g., stored in one or more machine-readable media) that embody any one or more aspects and / or methodologies of this disclosure. In another example, memory 808 may further include, but is not limited to, an operating system, one or more application programs, other program modules, program data, and any number of program modules, including any combination thereof.

[0137] The computer system 800 may also include a storage device 824. Examples of storage devices (e.g., storage device 824) include, but are not limited to, hard disk drives, magnetic disk drives, optical disk drives combined with optical media, solid-state memory devices, and any combination thereof. The storage device 824 may be connected to the bus 812 by a suitable interface (not shown). Exemplary interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE®), and any combination thereof. In one example, the storage device 824 (or one or more of its components) may be detachably interfaced with the computer system 800 (e.g., via an external port connector (not shown)). In particular, the storage device 824 and associated machine-readable media 828 can provide non-volatile and / or volatile storage for machine-readable instructions, data structures, program modules, and / or other data for the computer system 800. In one example, the software 820 may reside entirely or partially within a machine-readable medium 828. In another example, the software 820 may reside entirely or partially within a processor 804.

[0138] Computer system 800 may also include an input device 832. In one example, a user of computer system 800 may input commands and / or other information to computer system 800 via the input device 832. Examples of input devices 832 include, but are not limited to, alphanumeric input devices (e.g., keyboards), pointing devices, joysticks, gamepads, audio input devices (e.g., microphones, voice response systems, etc.), cursor control devices (e.g., mice), touchpads, optical scanners, video capture devices (e.g., still cameras, video cameras), touchscreens, and any combination thereof. Input device 832 may interface to bus 812 via any of a variety of interfaces (not shown) including, but not limited to, serial interfaces, parallel interfaces, game ports, USB interfaces, FIREWIRE® interfaces, direct interfaces to bus 812, and any combination thereof. Input device 832 may include a touchscreen interface, which may be part of or separate from display 836, as will be further described below. As described above, the input device 832 can be used as a user selection device for selecting one or more graphical representations within the graphical interface.

[0139] The user can also input commands and / or other information into the computer system 800 via a storage device 824 (e.g., a removable disk drive, a flash drive, etc.) and / or a network interface device 840. Network interface devices such as network interface device 840 can be used to connect the computer system 800 to one or more of various networks, such as network 844, and one or more remote devices 848 connected to them. Examples of network interface devices include, but are not limited to, network interface cards (e.g., mobile network interface cards, LAN cards), modems, and any combination thereof. Examples of networks include, but are not limited to, wide area networks (e.g., the Internet, a corporate network), local area networks (e.g., a network associated with an office, building, campus, or other relatively small geographical space), telephone networks, data networks associated with telephone / voice operators (e.g., mobile carrier data and / or voice networks), direct connections between two computing devices, and any combination thereof. A network, such as network 844, can use wired and / or wireless communication modes. In general, any network topology can be used. Information (e.g., data, software 820, etc.) can be communicated to and from the computer system 800 via the network interface device 840.

[0140] The computer system 800 may further include a video display adapter 852 for communicating displayable images to a display device such as a display device 836. Examples of display devices include, but are not limited to, liquid crystal displays (LCDs), cathode ray tubes (CRTs), plasma displays, light-emitting diode (LED) displays, and any combination thereof. The display adapter 852 and the display device 836 may be used in conjunction with a processor 804 to provide a graphical representation of the embodiments of this disclosure. In addition to the display devices, the computer system 800 may include one or more other peripheral output devices, including, but not limited to, audio speakers, printers, and any combination thereof. Such peripheral output devices may be connected to the bus 812 via a peripheral interface 856. Examples of peripheral interfaces include, but are not limited to, serial ports, USB connections, FIREWIRE® connections, parallel connections, and any combination thereof.

[0141] The above has been a detailed description of exemplary embodiments of the present invention. Various modifications and additions can be made without departing from the spirit and scope of the invention. Each feature of the various embodiments described above can be combined with features of other described embodiments as needed to provide a number of feature combinations in relevant new embodiments. Furthermore, although several distinct embodiments have been described above, those described herein are merely illustrative of the application of the principles of the present invention. Furthermore, certain methods herein can be illustrated and / or described as being performed in a particular order, but the order will vary among those skilled in the art to achieve the embodiments provided herein. Therefore, this description is intended to be construed as illustrative only and will not limit the scope of the invention.

[0142] In the above description and claims, phrases such as “at least one of” or “one or more of” may appear, followed by a concatenated list of elements or features. The term “and / or” may also appear in lists of two or more elements or features. Unless implicitly or explicitly contradicted by the context in which it is used, such phrases are intended to mean any of the enumerated elements or features individually, or any of the enumerated elements or features in combination with any of the other enumerated elements or features. For example, the phrases “at least one of A and B,” “one or more of A and B,” and “A and / or B” are intended to mean “A only, B only, or both A and B,” respectively. A similar interpretation is intended for lists containing three or more items. For example, the phrases “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, and / or C” are intended to mean “A only, B only, C only, A and B, A and C, B and C, or A, B and C,” respectively. Furthermore, the use of the term “based on” in the foregoing and in the claims is intended to mean “at least partially based,” so as to allow for features or elements that are not enumerated.

[0143] The subject matter described herein can be implemented in systems, apparatus, methods, and / or articles, depending on the desired configuration. The embodiments described above do not represent all embodiments that correspond to the subject matter described herein. Rather, they are merely some examples that correspond to aspects relating to the subject matter described herein. While several variations have been described in detail above, other modifications or additions are also possible. In particular, further features and / or variations can be provided in addition to those described herein. For example, the embodiments described above may cover various combinations and partial combinations of the disclosed features, and / or combinations and partial combinations of some of the further features described above. Furthermore, the logical flows shown in the accompanying drawings and / or described herein do not necessarily require a specific order or sequence shown to achieve the desired result. Other embodiments may be within the scope of the following claims.

[0144] [Cross-reference of related applications]

[0145] This application claims priority to U.S. Nonprovisional Application No. 18 / 430,863, filed on February 2, 2024, which is a continuation application of Nonprovisional Application No. 18 / 226,058, filed on July 25, 2023, titled "IMAGING DEVICE AND A METHOD FOR IMAGE GENERATION OF A SPECIMEN," now issued on May 14, 2024, U.S. Patent No. 11,983,874, each of which is incorporated herein by reference in whole.

Claims

1. An imaging device for generating an image of a sample, wherein the imaging device is A first set of parameters related to the first location of the sample is received, A second set of parameters related to at least one second location of the sample is received. According to the first parameter set, a first set of images of a first target area at the first location of the sample is captured, and according to the second parameter set, a second set of images of a second target area at the second location of the sample is captured. The first set of images is compiled into a first integrated image, and the second set of images is compiled into a second integrated image. The first integrated image and the second integrated image are combined into a combined image. An imaging device comprising a circuit configuration configured to display the aforementioned combined image.

2. The imaging device according to claim 1, wherein the first parameter set comprises one or more focal lengths associated with each of the first plurality of images.

3. The imaging device according to claim 1, wherein the first parameter set comprises parameters related to some of the first plurality of images.

4. The imaging device according to claim 1, wherein the first parameter set comprises nominal focal lengths associated with the first plurality of images.

5. The imaging device according to claim 1, wherein the circuit configuration is further configured to process the first plurality of images, and processing the first plurality of images is comprising determining the degree of quality of depiction of a target area of ​​one or more of the first plurality of images.

6. The aforementioned circuit configuration is The first integrated image is compared with a goodness-of-fit measure, The imaging device according to claim 1, further configured to flag the first integrated image if the first integrated image is outside a predetermined threshold of the goodness of fit scale.

7. Comparing the first integrated images comprises using a goodness-of-fit machine learning model, and using the goodness-of-fit machine learning model, Training a goodness-of-fit machine learning model using goodness-of-fit training data which includes multiple data entries, each containing multiple integrated image inputs correlated to a goodness-of-fit output, The process includes using the trained goodness-of-fit machine learning model to flag the first integrated image in accordance with a comparison between the first integrated image and the goodness-of-fit measure, The imaging device according to claim 6.

8. The aforementioned circuit configuration is If the first integrated image is flagged, update the first multiple images. The updated first set of images are compiled to create an updated integrated image. The updated integrated image is compared with the goodness-of-fit scale, The imaging device according to claim 6, further configured to identify an optimized integrated image if the updated integrated image falls within the predetermined threshold of the goodness-of-fit measure.

9. The imaging device according to claim 1, wherein the circuit configuration is further configured to move the optical system from the first location to the second location according to the second set of parameters.

10. A method for generating an image of a specimen, wherein the method is The imaging device receives a first set of parameters related to the first location of the sample, The imaging device receives a second set of parameters related to the second location of the sample, The imaging device captures a first set of images of a first target area at a first location of the sample according to the first parameter set, and a second set of images of a second target area at a second location of the sample according to the second parameter set. The processor compiles the first plurality of images into a first integrated image, and the second plurality of images into a second integrated image, The processor combines the first integrated image and the second integrated image into a combined image, A method comprising displaying the combined image using the aforementioned processor.

11. The method according to claim 10, wherein the first parameter set comprises one or more focal lengths associated with each of the first plurality of images.

12. The first parameter set comprises parameters related to some of the first plurality of images, The method according to claim 10.

13. The method according to claim 10, wherein the first parameter set comprises nominal focal lengths associated with the first plurality of images.

14. The method according to claim 10, further comprising processing the first plurality of images, wherein processing the first plurality of images comprises determining the degree of quality of depiction of a target area of ​​one or more of the first plurality of images.

15. The processor compares the first integrated image with a goodness-of-fit measure, The first integrated image is further flagged if it falls outside a predetermined threshold of the goodness-of-fit measure. The method according to claim 10.

16. Comparing the first integrated images comprises using a goodness-of-fit machine learning model, and using the goodness-of-fit machine learning model, Training a goodness-of-fit machine learning model using goodness-of-fit training data which includes multiple data entries, each containing multiple integrated image inputs correlated to a goodness-of-fit output, The process includes flagging the first integrated image in accordance with a comparison between the first integrated image and the goodness-of-fit measure using the trained goodness-of-fit machine learning model, The method according to claim 15.

17. If the first integrated image is flagged by the processor, the first multiple images are updated. The method further comprises compiling the updated first set of images to create an optimized integrated image. The method according to claim 15.

18. The method according to claim 10, further comprising moving the optical system from the first location to the second location according to the second set of parameters by an imaging system.

19. An imaging device for generating an image of a sample, wherein the imaging device is Optical systems and, A slide, comprising a slide port configured to hold the slide on which a specimen is placed, An actuator mechanism mechanically connected to a moving element, Processing module and, At least one processor, A memory that is communicably connected to at least one processor, wherein the memory is A first parameter set related to the first location of the sample is received, A second set of parameters related to the second location of the sample is received, Using the optical system, a plurality of first images of a first target area at the first location of the specimen are captured according to the first parameter set. Using the aforementioned optical system, the first multiple layers are compiled into a first integrated image. Using the optical system, a second set of images of a second target area at the second location of the specimen are captured according to the second parameter set. Using the aforementioned at least one processor, the second set of layers is compiled into a second integrated image. The combined images from Series 1 and Series 2 are combined into a single composite image. An imaging device comprising memory and an output interface, including instructions for configuring the at least one processor to display the combined image to a user.

20. Moving the optical element from the first location to the second location is Using the actuator mechanism, the slide is positioned relative to the optical system such that the first target area defined by the first parameter set lies within the line of sight or line of sight of the optical sensor of the optical system, The imaging device according to claim 19, further comprising setting the zoom of the optical system based on the zoom level specified by the first parameter set.

21. An imaging device for generating an image of a sample, wherein the imaging device is A first set of parameters related to the first location of the sample is received, A second set of parameters related to at least one second location of the sample is received. According to the first parameter set, a first set of images of a first target area at the first location of the sample is captured, and according to the second parameter set, a second set of images of a second target area at the second location of the sample is captured. The first set of images is compiled into a first integrated image, and the second set of images is compiled into a second integrated image. The first integrated image is compared with a goodness-of-fit measure, If the first integrated image is outside a predetermined threshold of the goodness-of-fit measure, the first integrated image is flagged. The combined images from Series 1 and Series 2 are combined into a single composite image. The circuit configuration includes a circuit configured to display the aforementioned combined image, Comparing the first integrated images described above involves using a goodness-of-fit machine learning model. An imaging device in which using the goodness-of-fit machine learning model comprises training the goodness-of-fit machine learning model using goodness-of-fit scale training data which includes a plurality of data entries including a plurality of integrated image inputs correlated to a goodness-of-fit scale output, and using the trained goodness-of-fit machine learning model to flag the first integrated image in accordance with a comparison between the first integrated image and the goodness-of-fit scale.

22. The imaging device according to claim 21, wherein the first parameter set comprises one or more focal lengths associated with each of the first plurality of images.

23. The imaging device according to claim 21, wherein the first parameter set comprises parameters related to some of the first plurality of images.

24. The imaging device according to claim 21, wherein the first parameter set comprises nominal focal lengths associated with the first plurality of images.

25. The imaging device according to claim 21, wherein the circuit configuration is further configured to process the first plurality of images, and processing the first plurality of images comprises determining the degree of quality of depiction of a target area of ​​one or more of the first plurality of images.

26. The aforementioned circuit configuration is If the first integrated image is flagged, update the first multiple images. The updated first set of images are compiled to create an updated integrated image. The updated integrated image is compared with the goodness-of-fit scale, The imaging device according to claim 21, further configured to identify an optimized integrated image if the updated integrated image falls within the predetermined threshold of the goodness-of-fit measure.

27. The imaging device according to claim 21, wherein the circuit configuration is further configured to move the optical system from the first location to the second location according to the second set of parameters.

28. A method for generating an image of a specimen, wherein the method is The imaging device receives a first set of parameters related to the first location of the sample, The imaging device receives a second set of parameters related to the second location of the sample, The imaging device captures a first set of images of a first target area at a first location of the sample according to the first parameter set, and a second set of images of a second target area at a second location of the sample according to the second parameter set. The processor compiles the first plurality of images into a first integrated image, and the second plurality of images into a second integrated image, The processor compares the first integrated image with a goodness-of-fit measure, The processor flags the first integrated image if the first integrated image is outside a predetermined threshold of the goodness of fit measure. The processor combines the first integrated image and the second integrated image into a combined image, The processor is configured to display the combined image, Comparing the first integrated images described above involves using a goodness-of-fit machine learning model. A method comprising using the goodness-of-fit machine learning model, which includes training the goodness-of-fit machine learning model using goodness-of-fit scale training data, which includes a plurality of data entries, each of which includes a plurality of integrated image inputs correlated to a goodness-of-fit scale output, and using the trained goodness-of-fit machine learning model to flag the first integrated image in accordance with a comparison between the first integrated image and the goodness-of-fit scale.

29. The method according to claim 28, wherein the first parameter set comprises one or more focal lengths associated with each of the first plurality of images.

30. The method according to claim 28, wherein the first parameter set comprises parameters relating to some of the first plurality of images.

31. The method according to claim 28, wherein the first parameter set comprises nominal focal lengths associated with the first plurality of images.

32. The method according to claim 28, further comprising processing the first plurality of images, wherein processing the first plurality of images comprises determining the degree of quality of depiction of a target area of ​​one or more of the first plurality of images.

33. If the first integrated image is flagged by the processor, the first multiple images are updated. The method further comprises compiling the updated first set of images to create an optimized integrated image. The method according to claim 28.

34. The method according to claim 28, further comprising moving the optical system from the first location to the second location according to the second set of parameters by an imaging system.

35. An imaging device for generating an image of a sample, wherein the imaging device is Optical systems and, A slide, comprising a slide port configured to hold the slide on which a specimen is placed, At least one processor, A memory that is communicably connected to at least one processor, wherein the memory is A first parameter set related to the first location of the sample is received, A second set of parameters related to the second location of the sample is received, Using the optical system, a plurality of first images of a first target area at the first location of the specimen are captured according to the first parameter set. The first set of layers are compiled into a first unified image. The first integrated image is compared with a goodness-of-fit measure, If the first integrated image is outside a predetermined threshold of the goodness-of-fit measure, the first integrated image is flagged. Using the optical system, a second set of images of a second target area at the second location of the specimen are captured according to the second parameter set. The second set of layers is compiled into a second unified image. The combined images from Series 1 and Series 2 are combined into a single composite image. The system includes a memory and a memory, which includes instructions that configure the at least one processor to display the combined image to the user using an output interface. Comparing the first integrated images described above involves using a goodness-of-fit machine learning model. An imaging device in which using the goodness-of-fit machine learning model comprises training the goodness-of-fit machine learning model using goodness-of-fit scale training data which includes a plurality of data entries including a plurality of integrated image inputs correlated to a goodness-of-fit scale output, and using the trained goodness-of-fit machine learning model to flag the first integrated image in accordance with a comparison between the first integrated image and the goodness-of-fit scale.

36. The imaging device further comprises an actuator mechanism and captures the first plurality of images, Using the actuator mechanism, the slide is positioned relative to the optical system such that the first target area defined by the first parameter set lies within the line of sight or line of sight of the optical sensor of the optical system, The imaging device according to claim 35, further comprising setting the zoom of the optical system based on the zoom level specified by the first parameter set.

37. An imaging device for generating an image of a sample, wherein the imaging device is According to a first parameter set, a first set of images of a first location within a first target area of ​​the sample are captured. A first integrated image is compiled according to the first plurality of images. A second set of images of a second location within the first target area of ​​the sample is captured according to the second parameter set. The aforementioned second set of images are compiled into a second integrated image. The combined images from Series 1 and Series 2 are combined into a single composite image. The circuit configuration includes a circuit configured to display the aforementioned combined image, An imaging device wherein the first parameter set comprises a first plurality of focal lengths, at least one of the first plurality of focal lengths corresponds to at least one of the first plurality of images, and compiling the first integrated image comprises removing a first artifact in the first plurality of images using an artifact machine learning model comprising a generative machine learning model, and the second parameter set comprises a second plurality of focal lengths, at least one of the second plurality of focal lengths corresponds to at least one of the second plurality of images.

38. Removing the first artifact mentioned above is Inputting the aforementioned first set of images into the artifact machine learning model, The imaging device according to claim 37, further comprising removing the first artifact in the first plurality of images using the artifact removal machine learning model.

39. The aforementioned imaging device, A third parameter set relating to a second target area of ​​the sample, wherein a third parameter set having a third depth of field is received, A third set of images of the second target area of ​​the sample is captured according to the third parameter set. The imaging device according to claim 37, comprising a circuit configuration configured to compile the third plurality of images into a third integrated image.

40. The imaging device according to claim 39, wherein the imaging device has a circuit configuration configured to combine the first integrated image, the second integrated image, and the third integrated image into the combined image.

41. The imaging device according to claim 39, wherein the imaging device comprises a circuit configuration configured to move an optical system from a first target area to a second target area according to the third parameter set.

42. The imaging device according to claim 37, wherein the second plurality of focal lengths do not have the same spread as the first plurality of focal lengths.

43. The imaging device according to claim 37, wherein the imaging device comprises a circuit configuration configured to move the optical system from the first location to the second location.

44. The aforementioned imaging device, The first integrated image is compared with a goodness-of-fit measure, The imaging device according to claim 37, further comprising a circuit configuration configured to flag the first integrated image if the first integrated image is outside a predetermined threshold of the goodness of fit scale.

45. Comparing the first integrated images described above involves using a goodness-of-fit machine learning model. Using the aforementioned goodness-of-fit machine learning model, Training a goodness-of-fit machine learning model using goodness-of-fit training data which includes multiple data entries, each including an example of a combined image as input that correlates with an example of a goodness-of-fit scale as output; The imaging device according to claim 44, further comprising flagging the integrated image in accordance with the comparison using the trained goodness-of-fit machine learning model.

46. The aforementioned imaging device, If the first integrated image is flagged, update the first multiple images. The updated first set of images are compiled to create an updated integrated image. The updated integrated image is compared with the goodness-of-fit scale, The imaging device according to claim 44, further comprising a circuit configuration configured to identify an optimized integrated image if the updated integrated image falls within a predetermined threshold of the goodness-of-fit measure.

47. A method for using an imaging device for generating an image of a specimen, wherein the method is Using an imaging device, capture a first set of images of a first location within a first target area of ​​a sample according to a first parameter set, Compiling a first integrated image according to the first plurality of images using the imaging device, Using the imaging device, capture a second set of images of a second location within the first target area of ​​the sample according to the second parameter set. Compiling the second set of images into a second integrated image using the imaging device, Using the imaging device, the first integrated image and the second integrated image are combined into a combined image. The system includes displaying the combined image using a graphical user interface, A method comprising: the first parameter set comprising a first plurality of focal lengths, at least one of the first plurality of focal lengths corresponding to at least one of the first plurality of images, and compiling the first integrated image comprising removing a first artifact in the first plurality of images using the imaging device and an artifact machine learning model comprising a generative machine learning model, wherein the second parameter set comprises a second plurality of focal lengths, at least one of the second plurality of focal lengths corresponding to at least one of the second plurality of images.

48. Removing the first artifact and the second artifact is Inputting the aforementioned first set of images into the artifact machine learning model, The method according to claim 47, further comprising using the artifact removal machine learning model to remove the first artifact in the first plurality of images and the second artifact in the second plurality of images.

49. The method described above is The imaging device receives a third parameter set relating to a second target area of ​​the sample, the third parameter set comprising a third depth of field, The imaging device captures a third set of images of the second target area of ​​the specimen according to the third parameter set, The method according to claim 47, further comprising compiling the third plurality of images into a third integrated image using the imaging device.

50. The method according to claim 49, further comprising combining the first integrated image, the second integrated image, and the third integrated image into the combined image using the imaging device.

51. The method according to claim 49, further comprising moving the optical system from the first target area to the second target area according to the third parameter set by the imaging device.

52. The method according to claim 47, wherein the second plurality of focal lengths do not have the same spread as the first plurality of focal lengths.

53. The method according to claim 47, further comprising moving the optical system from the first location to the second location using the imaging device.

54. The method described above is The imaging device compares the first integrated image with a goodness-of-fit scale, The method according to claim 47, further comprising the imaging device flagging the first integrated image if the first integrated image is outside a predetermined threshold of the goodness-of-fit measure.

55. It is equipped to use a goodness-of-fit machine learning model, Using the aforementioned goodness-of-fit machine learning model, Training a goodness-of-fit machine learning model using goodness-of-fit training data which includes multiple data entries, each including an example of a combined image as input that correlates with an example of a goodness-of-fit scale as output; The method according to claim 54, further comprising flagging the integrated image in accordance with the comparison using the trained goodness-of-fit machine learning model.

56. The method described above is If the first integrated image is flagged by the imaging device, the first multiple images are updated. The imaging device compiles the updated first multiple images to create an updated integrated image, The imaging device compares the updated integrated image with the goodness-of-fit scale, The method of claim 54, further comprising: the imaging device identifying an optimized integrated image if the updated integrated image falls within the predetermined threshold of the goodness-of-fit measure.

Images